heart-rate

Heart Rate Estimation Using Computer Vision

Posted on by Zbigatron

This post is another that has been inspired by a forum question: “What are some lesser known use cases for computer vision?” I jumped at the opportunity to answer this question because if there’s one thing I’m proud of with respect to this blog, it is the weird and wacky use cases that I have documented here.

Some things I’ve talked about include:

In this post I would like to add to the list above and discuss another lesser known use case for computer vision: heart rate estimation from colour cameras.

Vital Signal Estimation

Heart rate estimation belongs in the field called “Vital Signal Estimation” (VSE). In computer vision, VSE has been around for a while. One of the more famous attempts at this comes from 2012 from a paper entitled “Eulerian Video Magnification for Revealing Subtle Changes in the World” that was published at SIGGRAPH by MIT.

(Note: as I’ve mentioned in the past, SIGGRAPH, which stands for “Special Interest Group on Computer GRAPHics and Interactive Techniques”, is a world-renowned annual conference held for computer graphics researchers. But you do sometimes get papers from the world of computer vision being published there as is the case with this one.)

Basically, the way that VSE was implemented by MIT was that they analysed images captured from a camera for small illumination changes on a person’s face produced by varying amounts of blood flow to it. These changes were then magnified to make it easier to scrutinise. See, for example, this image from their paper:

h-r-estimation
(image source)

Amazing that these illumination changes can be extracted like this, isn’t it?

This video describes the Eulerian Video Magnification technique developed by these researchers (colour amplification begins at 1:25):

Interestingly, most research in VSE has focused around this idea of magnifying minute changes to estimate heart rates.

Uses of Heart Rate Estimation

What could heart rate estimation by computer vision be used for? Well, medical scenarios automatically come to mind because of the non-invasive (i.e. non-contact) feature of this technique. The video above (from 3:30) suggests using this technology for SIDS detection. Stress detection is also another use case. And what follows from this is lie detection. I’ve already written about lie detection using thermal imaging – here is one more way for us to be monitored unknowingly.

On the topic of being monitored, this paper suggests (using a slightly different technique of magnifying minute changes in blood flow to the face) detecting emotional reactions to TV programs and advertisements.

Ugh! It’s just what we need, isn’t it? More ways of being watched.

Making it Proprietary

From what I can see, VSE in computer vision is still mostly in the research domain. However, this research from Utah State University has recently been patented and a company called Photorithm Inc. formed around it. The company produces baby monitoring systems to detect abnormal breathing in sleeping infants. In fact, Forbes wrote an interesting article about this company this year. Among other things, the article talks about the reasons behind the push by the authors for this research and how the technology behind the application works. It’s a good read.

Here’s a video demonstrating how Photorithm’s product works:

Summary

This post talked about another lesser known use case of computer vision: heart rate estimation. MIT’s famous research from 2012 was briefly presented. This research used a technique of magnifying small changes in an input video for subsequent analysis. Magnifying small changes like this is how most VSE technologies in computer vision work today.

After this, a discussion of what heart rate estimation by computer vision could be used for followed. Finally, it was mentioned that VSE is still predominantly something in the research domain, although one company has recently appeared on the scene that sells baby monitoring systems to detect abnormal breathing in sleeping infants. The product being sold by this company uses computer vision techniques presented in this post.

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steganography-example

Image Steganography – Simple Examples

Posted on by Zbigatron

In my last post I introduced the field of image steganography, which is the practice of concealing secret messages in digital images. I looked at the history of steganography and presented some recently reported real-life cases (including one from the FBI) where digital steganography was used for malicious purposes.

In this post I would like to present to you the following two very simple ways messages can be hidden in digital images:

  • JPEG Concealing
  • Least Significant Bit Technique

These techniques, although trivial and easy to detect, will give you an idea of how simple (and therefore potentially dangerous) digital image steganography can be.

JPEG Concealing

Image files in general are composed of two sections: header data + image data. The header data section can contain metadata information pertaining to the image such as date of creation, author, image resolution, and compression algorithm used if the image is compressed. This is the standard for JPEGs, BMPs, TIFFs, GIFs, etc.

Knowing this, one can work around these file structures to conceal messages. 

Let’s take JPEGs as an example. The file structure for this format is as follows:

jpeg-file-structure

Notice that every single JPEG file starts and ends with the SOI and EOI markers, respectively.

What this means is that any image interpreting application (e.g. Photoshop or GIMP, any internet browser, the standard photo viewing software that comes with your operating system, etc.) looks for these markers inside the file and knows that it should interpret and display whatever comes between them. Everything else is automatically ignored. 

Hence, you can insert absolutely anything after the EOI marker like this:

jpeg-file-structure-with-msg

And even though the hidden message will be part of the JPEG file and travel with this file wherever it goes, no standard application will see anything out of the ordinary. It will just read whatever comes before EOI.

Of course, if you put a lot of data after EOI, your file size will increase significantly and might, therefore, arouse suspicion – so you have to be wary of that. In this case, it might be an idea to use a high resolution JPEG file (that naturally has a large file size) to turn attention away from your hidden message.

If you would like to try this steganography technique out yourself, download a hex editor for your machine (if you use Windows, WinHex is a good program), search for FF D9 (which is the hex version of EOI), paste anything you want after this section marker, and save your changes. You will notice that the file is opened like any other JPEG file. The hidden message simply piggy backs on top of the image file. Quite neat!

(Note: hexadecimal is a number system made up of 16 symbols. Our decimal system uses 10 digits: 0-9. The hex system uses the 10 digits from the decimal system plus the first 6 letters of the alphabet. To cut a long story short, hexadecimal is a shorthand and therefore much easier way to read/write binary digits, i.e. 1s and 0s. Most file formats will not save data in human readable form and we therefore need help if we want to view the raw data of these files – this is why hex is used sometimes used)

The Least Significant Bit Technique

Although easy to detect (if you know what you’re looking for), the Least Significant Bit (LSB) technique is a very sly way of hiding data in images. The way that it works is by taking advantage of the fact that small changes in pixel colour are invisible to the naked eye.

Let’s say we’re encoding images in the RGB colour space – i.e. each pixel’s colour is represented by a combination of a certain amount of red (R), a certain amount of green (G), and a certain amount of blue (B). The amount of red, green, and blue is given in the the range of 0 to 255. So, pure red would be represented as (255, 0, 0) in this colour space – i.e. the maximum amount of red, no green, and no blue.

Now, in this scenario (and abstracting over a few things), a machine would represent each pixel in 3 bytes – one byte for each of red, green and blue. Since a byte is 8 bits (i.e. 8 ones and zeros) each colour would be stored as something like this:

rgb_red-bits

That would be the colour red (11111111 in binary is 255 in our number system).

What about if we were to change the 255 into 254 – i.e. change 11111111 into 11111110? Would we notice the difference in the colour red? Absolutely not. How about  changing 11111111 to 11111100 (255 to 252)? We still would not notice the difference – especially if this change is happening to single pixels!

The idea behind the LSB technique, then, is to use this fact that slightly changing the colour of each pixel would be imperceptible to the naked eye.

Since the last few digits in a byte are insignificant this is where LSB gets its name: the Least Significant Bit technique.

We know, then, that the last few bits in each byte can be manipulated. So, we can use this knowledge to set aside these bits of each pixel to store a hidden message.

Let’s look at an example. Suppose we want to hide a message like “SOS“. We choose to use the ASCII format to encode our letters. In this format each character has its own binary representation. The binary for our message would be:

ASCII-sos-message

What we do now is split each character into two-bit pairs (e.g. S has the following four pairs: 01, 01, 00, 11) and spread these pairs successively along multiple pixels. So, if our image had four pixels, our message would be encoded like this:

lsb-encoding-example

Notice that each letter is spread across two pixels: one pixel encodes the first 3 pairs and the next pixel takes the last pair. Very neat, isn’t it? You can choose to use more than 2 bits per pixel to store your message but remember that by using more bits you risk changes to each pixel becoming perceptible.

Also, the larger the image, the more you can encode. And, since images can be represented in binary, you can store an image inside an image using the exact same technique. In this respect, I would highly recommend that you take a look at this little website that will allow you to do just that using the method described here.

I would also recommend going back to the first post of this series and looking at the image-inside-image steganography examples there provided by the FBI. It shows brilliantly how sneaky image steganography can be.

Summary

In this post I looked at two simple techniques of image steganography. The first technique takes advantage of the fact that image files have an end-of-file (EOF) marker in their metadata. This means that any program opening these images will read everything up to and including this marker. If you were to put anything after the EOF, it would be hidden from view. The second technique takes advantage of the fact that slightly changing the colour of a pixel is imperceptible to the naked eye. In this sense, the least significant bits of each pixel can be used to spread a message (e.g. text or image) across the pixels in an image. A program would then be used to extract this message.

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Image Steganography – An Introduction

Posted on by Zbigatron

In this post (part 1 of 2 – part 2 can now be found here) I would like to introduce the topic of image steganography, which is the practice of concealing secret messages in digital images. I’ve always been fascinated by this subject so I have taken the excuse to research for this post as a way to delve into the topic. Turns out that image steganography is a fascinating field that should be garnering much more attention than it is.

I will divide the post into two sections:

  1. Steganography: what it is and its early history
  2. Digital image steganography and some recently reported real-life cases – including one from an FBI report on Russian spying in the US (like something out of the cold war)

In my next post I will detail some simple techniques of hiding messages in images, so stay tuned for that.

What is Steganography

Usually today if we want to send sensitive data (e.g. credit card information), we encrypt this data before sending it across the internet. Sending messages like this, however, can arouse suspicion: there is obviously sensitive/secret data in your encrypted message that you are trying to conceal. Attackers know exactly where to look to try to obtain this information.

But steganography works differently: you hide the message in plain sight in order for your message to not attract any attention at all.

The first recorded case of steganography goes back to 499 BC when the Greek tyrant Histiaeus shaved the head of his slave and “marked” (probably tattooed) a secret message onto it. The message was intended for Aristagoras and it was telling him to start a revolt against the Persians. Histiaeus waited for the slave’s hair to grow back before sending him on his way. When the slave reached Aristagoras, his head was shaved again to reveal the hidden message.

bust
(image source)

Who would have thought to stop the slave and look for a hidden message tattooed on his head? Ingenious, wasn’t it? (Well, maybe not for the slave who was probably left with that message permanently on his head…).

That’s the way steganography works: through deception.

It is an important topic because of how seemingly common it is becoming. A report from 2017 by the global computer security software company McAfee says that steganography is being used in more ways today than ever before. However, Simon Wiseman, the chief technology officer of the network security firm Deep Secure, argues that it’s not so much that steganography is becoming more popular, just that we are discovering it more often by learning how it is being done: “now that people are waking up to the fact that it’s out there, the discovery rate is going up.”

Either way, as McAfee claims: “Steganography will continue to become more popular.”

Digital Image Steganography

As mentioned earlier, digital image steganography is the hiding of secret messages inside images. Take a look at these two images distributed by the FBI:

steg-eg1

steg-eg2

You wouldn’t think that both of them contain the following map of an airport, would you?

steg-eg3

Well, they do. The FBI doesn’t lie 🙂

It’s a scary thing when you consider the huge number of images being sent across the internet every day. You would really have to know precisely where to scan for this stuff and what to look for otherwise you’re searching for a needle in a haystack.

Now, the first recorded case of image steganography in a cyberattack dates back to 2011. It was called the Duqu malware attack and it worked by encrypting and embedding data into small JPEG image files. These files were then sent to servers to obtain sensitive information (rather than doing destructive work directly like deleting files). McAfee says that it was used to, for example, steal digital certificates from its victims. How Duqu worked exactly, however, remains unknown. Researchers are still trying to work this out (although all sources I could find on this are fairly outdated). Quite amazing.

I found earlier reported cases, however, of image steganography being used for malicious purposes, not necessarily in cyberattacks. My favourite one is from the FBI.

Here’s an official report from them from 2010 accusing the Russian foreign intelligence agency of embedding encrypted text messages inside image files for communications with agents stationed abroad. This reportedly all took place in the 90s in the US. Turns out that the 10 spies mentioned in the report later pleaded guilty to being Russian agents and were used as part of a spy swap between the U.S. and Russian governments. The FBI and the Russians… and spy swapping! Like something out of a movie. Shows you how serious the topic of digital image steganography is.

fbi_logo

You can see how this way of embedding communication in images is a much more sophisticated version of the “tattooing a message on a shaved head” example from Ancient Greece described above.

Is image steganography being used like this by ISIS to communicate secretly amongst each other? Chances are it is.

Early this year a communication tool was discovered called MuslimCrypt (poor choice of name, in my opinion). As Wired reports, the tool was found in a private, pro-ISIS Telegram channel on January 20.  It is dead simple to use (take a look at the video on the Wired page to see this for yourselves): you select an image, write a message in text form, select a password, and click one button to hide this message inside the image. This image can then be sent across the internet after which the recipient puts it into MuslimCrypt and with one click of a button retrieves the hidden message. Sneaky, dangerous stuff.

What would make detection even more difficult is a hidden message distributed over multiple images. Well, models for this already exist, as the academic paper “Distributed Steganography”  (Liao et al., International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2011) presents.

Moreover, a patent for distributed steganography was filed by a certain Charles Easttom II William in 2010. This image from the patent summarises distributed steganography nicely:

distributed-steganography-example
(Image adapted from the original patent)

Fascinating stuff, isn’t it?

Stay tuned for my next post where I will look in detail at some simple examples of digital image steganography. (Update: this new post can now be found here)

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image-colourisation-example7

Image Colourisation – Converting B&W Photos to Colour

Posted on by Zbigatron

I got another great academic publication to present to you – and this publication also comes with an online interactive website for you to use to your heart’s content. The paper is from the field of image colourisation. 

Image colourisation (or ‘colorization’ for our US readers :P) is the act of taking a black and white photo and converting it to colour. Currently, this is a tedious, manual process usually performed in Photoshop, that can typically take up to a month for a single black and white photo. But the results can be astounding. Just take a look at the following video illustrating this process to give you an idea of how laborious but amazing image colourisation can be:

Up to a month to do that for each image!? That’s a long time, right?



But then came along some researchers from the University of California in Berkeley who decided to throw some deep learning and computer vision at the task. Their work, published at the European Conference on Computer Vision in 2016, has produced a fully automatic image colourisation algorithm that creates vibrant and realistic colourisations in seconds. 

Their results truly are astounding. Here are some examples:

image-colourisation-example2

image-colourisation-example3

Not bad, hey? Remember, this is a fully automatic solution that is only given a black and white photo as input.

How about really old monochrome photographs? Here is one from 1936:

image-colourisation-example5

And here’s an old one of Marilyn Monroe:

image-colourisation-example6

Quite remarkable. For more example images, see the official project page (where you can also download the code).

How did the authors manage to get such good results? It’s obvious that deep learning (DL) was used as part of the solution. Why is it obvious? Because DL is ubiquitous nowadays – and considering the difficulty of the task, no other solution is going to come near. Indeed, the authors report that their results are significantly better than previous solutions.

What is intuitive is how they implemented their solution. One might choose to go down the standard route of designing a neural network that maps a black and white image directly to a colour image (see my previous post for an example of this). But this idea will not work here. The reason for it is that similar objects can have very different colours.

Let’s take apples as an example to explain this. Consider an image dataset that has four pictures of an apple: 2 pictures showing a yellow apple and 2 showing a red one. A standard neural network solution that just maps black and white apples to colour apples will calculate the average colour of apples in the dataset and colour the black and white photo this way. So, 2 yellow + 2 red apples will give you an average colour of orange. Hence, all apples will be coloured orange because this is the way the dataset is being interpreted. The authors report that going down this path will produce very desaturated (bland) results.

So, their idea was to instead calculate what the probability is of each pixel being a particular colour. In other words, each pixel in a black and white image has a list of percentages calculated that represent the probability of that particular pixel being each specific colour. That’s a long list of colour percentages for every pixel! The final colour of the pixel is then chosen from the top candidates on this list.

Going back to our apples example, the neural network would tell us that pixels belonging to the apple in the image would have a 50% probability of being yellow and 50% probability of being red (because our dataset consists of only red and yellow apples). We would then choose either of these two colours – orange would never make an appearance.

As is usually the case, ImageNet with its 1.3 million images (cf. this previous blog post that describes ImageNet) is used to train the neural network. Because of the large array of objects in ImageNet, the neural network can hence learn to colour many, many scenes in the amazing way that it does.

What is quite neat is that the authors have also set up a website where you can upload your own black and white photos to be converted by their algorithm into colour. Try it out yourself – especially if you have old photos that you have always wanted to colourise.

Ah, computer vision wins again. What a great area in which to be working and researching.

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image-completion-example

Image Completion from SIGGRAPH 2017

Posted on by Zbigatron

Oh, I love stumbling upon fascinating publications from the academic world! This post will present to you yet another one of those little gems that has recently fallen into my lap. It’s on the topic of image completion and comes from a paper published in SIGGRAPH 2017 entitled “Globally and Locally Consistent Image Completion” (project page can be found here).

(Note: SIGGRAPH, which stands for “Special Interest Group on Computer GRAPHics and Interactive Techniques”, is a world renowned annual conference held for computer graphics researchers. But you do sometimes get papers from the world of computer vision being published there as is the case with the one I’m presenting here.)

This post will be divided into the following sections:

  1. What is image completion and some of its prior weaknesses
  2. An outline of the solution proposed by the above mentioned SIGGRAPH publication
  3. A presentation of results

If anything, please scroll down to the results section and take a look at the video published by the authors of the paper. There’s some amazing stuff to be seen there!

1. What is image completion?

Image completion is a technique for filling-in target regions with alternative content. A major use for image completion is in the task of object removal where an object from a photo is erased and the remaining hole is automatically substituted with content that hopefully maintains the contextual integrity of the image.

Image completion has been around for a while. Perhaps the most famous algorithm in this area is called PatchMatch which is used by Photoshop in its Content Aware Fill feature. Take a look at this example image generated by PatchMatch after the flowers in the bottom right corner were removed from the left image:

patchmatch-example
An image completion example on a natural scene generated by PatchMatch

Not bad, hey? But the problem with existing solutions such as PatchMatch is that images can only be completed with textures that solely come from the input image. That is, calculations for what should be plugged into the hole are done using information obtained just from the input image. So, for images like the flower picture above, PatchMatch works great because it can work out that green leaves is the dominant texture and make do with that.

But what about more complex images… and faces as well? You can’t work out what should go into a gap in an image of a face just from its input image. This is an image completion example done on a face by PatchMatch:

patchmatch-example2
An image completion example on a face generated by PatchMatch

Yeah, not so good now, is it? You can see how trying to work out what should go into a gap from other areas of the input image is not going to work for a lot of cases like this.

2. Proposed solution

This is where the paper “Globally and Locally Consistent Image Completion” comes in. The idea behind it, in a nutshell, is to use a massive database of images of natural scenes to train a single deep learning network for image completion. The Places2 dataset is used for this, which contains over 8 million images of diverse natural scenes – a massive database from which the network basically learns the consistency inherent in natural scenes. This means that information to fill in missing gaps in images is obtained from these 8 million images rather than just one single image!

Once this deep neural network is trained for image completion, a GAN (Generative Adversarial Network) approach is utilised to further improve this network.

GAN is an unsupervised neural network training technique where one or more neural networks are used to mutually improve each other in the training phase. One neural network tries to fool another and all neural networks are updated according to results obtained from this step. You can leave these neural networks running for a long time and watch them improving each other.

The GAN technique is very common in computer vision nowadays in scenarios where one needs to artificially produce images that appear realistic. 

Two additional networks are used in order to improve the image completion network: a global and a local context discriminator network. The former discriminator looks at the entire image to assess if it is coherent as a whole. The latter looks only at the small area centered at the completed region to ensure local consistency of the generated patch. In other words, you get two additional networks assisting in the training: one for global consistency and one local consistency.

These two auxiliary networks return a result stating whether the generated image is realistic-looking or artificial. The image completion network then tries to generate completed images to fool the auxiliary networks into thinking that their real.

In total, it took 2 months for the entire training stage to complete on a machine with four high-end GPUs. Crazy!

The following image shows the solution’s training architecture:

image-completion-solution-architecture
Overview of architecture for training for image completion (image taken from original publication)

Typically, to complete an image of 1024 x 1024 resolution that has one gap takes about 8 seconds on a machine with a single CPU or 0.5 seconds on one with a decent GPU. That’s not bad at all considering how good the generated results are – see the next section for this.

3. Results

The first thing you need to do is view the results video released by the authors of the publication. Visit their project page for this and scroll down a little. I can provide a shorter version of this video from YouTube here:

As for concrete examples, let’s take a look at some faces first. One of these faces is the same from the PatchMatch example above.

image-completion-on-faces-examples
Examples of image completion on faces (image adapted from original publication)

How’s impressive is this?

My favourite examples are of object removal. Check this out:

image-completion-examples
Examples of image completion (image taken from original publication)

Look how the consistency of the image is maintained with the new patch in the image. It’s quite incredible!

My all-time favourite example is this one:

image-completion-example
Another example of image completion (taken from original publication)

Absolutely amazing. More results can be viewed in supplementary material released by the authors of the paper. It’s well-worth a look!

Summary

In this post I presented a paper on image completion from SIGGRAPH 2017 entitled “Globally and Locally Consistent Image Completion”. I first introduced the topic of image completion, which is a technique for filling-in target regions with alternative content, and described some weaknesses of previous solutions – mainly that calculations for what should be generated for a target region are done using information obtained just from the input image. I then presented the more technical aspect of the proposed solution as presented in the paper. I showed that the image completion deep learning network learnt about global and local consistency of natural scenes from a database of over 8 million images. Then, a GAN approach was used to further train this network. In the final section of the post I showed some examples of image completion as generated by the presented solution.

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The Baidu and ImageNet Controversy

Posted on by Zbigatron

Two months ago I wrote a post about some recent controversies in the industry in computer vision. In this post I turn to the world of academia/research and write about something controversial that occurred there.

But since the world of research isn’t as aggressive as that of the industry, I had to go back three years to find anything worth presenting. However, this event really is interesting, despite its age, and people in research circles talk about it to this day.

The controversy in question pertains to the ImageNet challenge and the Baidu research group. Baidu is one of the largest AI and internet companies in the world. Based in Beijing, it has the 2nd largest search engine in the world and is hence commonly referred to as China’s Google. So, when it is involved in a controversy, you know it’s no small matter!

I will divide the post into the following sections:

  1. ImageNet and the Deep Learning Arms Race
  2. What Baidu did and ImageNet’s response
  3. Ren Wu’s (Ex-Baidu Researcher’s) later response (here is where things get really interesting!)

Let’s get into it.

ImageNet and the Deep Learning Arms Race

(Note: I wrote about what ImageNet is in my last post, so please read that post for a more detailed explanation.) 

ImageNet is the most famous image dataset by a country mile. Currently there are over 14 million images in ImageNet for nearly 22,000 synsets (WordNet has ~100,000 synsets). Over 1 million images also have hand-annotated bounding boxes around the dominant object in the image.

However, when the term “ImageNet” is used in CV literature, it usually refers to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) which is an annual competition for object detection and image classification organised by computer scientists at Stanford University, the University of North Carolina at Chapel Hill and the University of Michigan.

This competition is very famous. In fact, the deep learning revolution of the 2010s is widely attributed to have originated from this challenge after a deep convolutional neural network blitzed the competition in 2012. Since then, deep learning has revolutionised our world and the industry has been forming research groups like crazy to push the boundary of artificial intelligence. Facebook, Amazon, Google, IBM, Microsoft – all the major players in IT are now in the research game, which is phenomenal to think about for people like me who remember the days of the 2000s when research was laughed at by people in the industry.

With such large names in the deep learning world, a certain “computing arms race” has ensued. Big bucks are being pumped into these research groups to obtain (and trumpet far and wide) results better than other rivals. Who can prove to be the master of the AI world? Who is the smartest company going around? Well, competitions such as ImageNet are a perfect benchmark for questions like this, which makes the ImageNet scandal quite significant.

Baidu and ImageNet

To have your object classification algorithm scored on the ImageNet Challenge, you first get it trained on 1.5 million images from the ImageNet dataset. Then, you submit your code to the ImageNet server where this code is tested against a collection of 100,000 images that are not known to anybody. What is key, though, is that to avoid people fine-tuning the parameters in their algorithms to this specific testing set of 100,000 images, ImageNet only allows 2 evaluations/submissions on the test set per week (otherwise you could keep resubmitting until you’ve hit that “sweet spot” specific to this test set).

Before the deep learning revolution, a good ILSVRC classification error rate was 25% (that’s 1 out of 4 images being classified incorrectly). After 2014, error rates have dropped to below 5%!

In 2015, Baidu announced that with its new supercomputer called Minwa it had obtained a record low error rate of 4.58%, which was an improvement on Google’s error rate of 4.82% as well as Microsoft’s of 4.9%. Massive news in the computing arms race, even though the error rate differences appear to be minimal (and some would argue, therefore, that they’re insignificant – but that’s another story).

However, a few days after this declaration, an initial announcement was made by ImageNet:

It was recently brought to our attention that one group has circumvented our policy of allowing only 2 evaluations on the test set per week.

Three weeks later, a follow up announcement was made stating that the perpetrator of this act was Baidu. ImageNet had conducted an analysis and found that 30 accounts connected to Baidu had been used in the period of November 28th, 2014 to May 13th, 2015 to make on average four times the permitted amount of submissions. 

As a result, ImageNet disqualified Baidu from that year’s competition and banned them from re-entering for a further 12 months.

Ren Wu, a distinguished AI scientist and head of the research group at the time, apologised for this mistake. A week later he was dismissed from the company. But that’s not the end of the saga.

Ren Wu’s Response

Here is where things get really interesting. 

A few days after being fired from Baidu, Ren Wu sent an email to Enterprise Technology in which he denied any wrongdoing:

We didn’t break any rules, and the allegation of cheating is completely baseless

Whoa! Talk about opening a can of worms!

Ren stated that there is “no official rule specify [sic] how many times one can submit results to ImageNet servers for evaluation” and that this regulation only appears once a submission is made from one account. From this he came to understand that 2 submissions per week can be made from each account/individual rather than a whole team. Since Baidu had 5 authors working on the project, he argues that he was allowed to make 10 submission per week.

I’m not convinced though because he still used 30 accounts (purportedly to be owned by junior students assisting in the research) to make these submissions. Moreover, he still admits that on two occasions the 10 submission threshold was breached – so, he definitely did break the rules.

Things get even more interesting, however, when he states that he officially apologised just for those two occasions as requested by his management:

A mistake in our part, and it was the reason I made a public apology, requested by my management. Of course, this was my biggest mistake. And things have been gone crazy since. [emphasis mine]

Whoa! Another can of worms. He apologised as a result of a request by his management and he states that this was a mistake. It looks like he’s accusing Baidu of using him as a scapegoat in this whole affair. Two months later he confirms this to the EE Times, by stating that

I think I was set up

Well, if that isn’t big news, I don’t know what is! I personally am not convinced by Ren’s arguments. But it at least shows that the academic/research world can be exciting at times, too 🙂

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object-detection-example

The Top Image Datasets and Their Challenges

Posted on by Zbigatron

In previous posts of mine I have discussed how image datasets have become crucial in the deep learning (DL) boom of computer vision of the past few years. In deep learning, neural networks are told to (more or less) autonomously discover the underlying patterns in classes of images (e.g. that bicycles are composed of two wheels, a handlebar, and a seat). Since images are visual representations of our reality, they contain the inherent complex intricacies of our world. Hence, to train good DL models that are capable of extracting the underlying patterns in classes of images, deep learning needs lots of data, i.e. big data. And it’s crucial that this big data that feeds the deep learning machine be of top quality.

In lieu of Google’s recent announcement of an update to its image dataset as well as its new challenge, in this post I would like to present to you the top 3 image datasets that are currently being used by the computer vision community as well as their associated challenges:

  1. ImageNet and ILSVRC
  2. Open Images and the Open Images Challenge
  3. COCO Dataset and the four COCO challenges of 2018

I wish to talk about the challenges associated with these datasets because challenges are a great way for researchers to compete against each other and in the process to push the boundary of computer vision further each year!

ImageNet

imagenet-logoThis is the most famous image dataset by a country mile. But confusion often accompanies what ImageNet actually is because the name is frequently used to describe two things: the ImageNet project itself and its visual recognition challenge.

The former is a project whose aim is to label and categorise images according to the WordNet hierarchy. WordNet is an open-source database for words that are organised hierarchically into synonyms. For example words like “dog” and “cat” can be found in the following knowledge structure:

WordNet-synset-graph
An example of a WordNet synset graph (image taken from here)

Each node in the hierarchy is called a “synonym set” or “synset”. This is a great way to categorise words because whatever noun you may have, you can easily extract its context (e.g. that a dog is a carnivore) – something very useful for artificial intelligence.

The idea with the ImageNet project, then, is to have 1000+ images for each and every synset in order to also have a visual hierarchy to accompany WordNet. Currently there are over 14 million images in ImageNet for nearly 22,000 synsets (WordNet has ~100,000 synsets). Over 1 million images also have hand-annotated bounding boxes around the dominant object in the image.

ImageNet-kit-fox
Example image of a kit fox from ImageNet showing hand-annotated bounding boxes

You can explore the ImageNet and WordNet dataset interactively here. I highly recommend you do this!

Note: by default only URLs to images in ImageNet are provided because ImageNet does not own the copyright to them. However, a download link can be obtained to the entire dataset if certain terms and conditions are accepted (e.g. that the images will be used for non-commercial research). 

Having said this, when the term “ImageNet” is used in CV literature, it usually refers to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) which is an annual competition for object detection and image classification. This competition is very famous. In fact, the DL revolution of the 2010s is widely attributed to have originated from this challenge after a deep convolutional neural network blitzed the competition in 2012.

The motivation behind ILSVRC, as the website says, is:

… to allow researchers to compare progress in detection across a wider variety of objects — taking advantage of the quite expensive labeling effort. Another motivation is to measure the progress of computer vision for large scale image indexing for retrieval and annotation.

The ILSVRC competition has its own image dataset that is actually a subset of the ImageNet dataset. This meticulously hand-annotated dataset has 1,000 object categories (the full list of these synsets can be found here) spread over ~1.2 million images. Half of these images also have bounding boxes around the class category object.

The ILSCVRC dataset is most frequently used to train object classification neural network frameworks such as VGG16, InceptionV3, ResNet, etc., that are publicly available for use. If you ever download one of these pre-trained frameworks (e.g. Inception V3) and it says that it can detect 1000 different classes of objects, then it most certainly was trained on this dataset.

Google’s Open Images

Google is a new player in the field of datasets but you know that when Google does something it will do it with a bang. And it has not disappointed here either.

Open Images is a new dataset first released in 2016 that contains ~9 million images – which is fewer than ImageNet. What makes it stand out is that these images are mostly of complex scenes that span thousands of classes of objects. Moreover, ~2 million of these images are hand-annotated with bounding boxes making Open Images by far the largest existing dataset with object location annotations. In this subset of images, there are ~15.4 million bounding boxes of 600 classes of object. These objects are also part of a hierarchy (see here for a nice image of this hierarchy) but one that is nowhere near as complex as WordNet.

open-images-eg
Open Images example image with bounding box annotation

As of a few months’ ago, there is also a challenge associated with Open Images called the “Open Images Challenge“. It is an object detection challenge and, what’s more interesting, there is also a visual relationship detection challenge (e.g. “woman playing a guitar” rather than just “guitar” and “woman”). The inaugural challenge will be held at this year’s European Conference on Computer Vision. It looks like this will be a super interesting event considering the complexity of the images in the dataset and, as a result, I foresee this challenge to be the de facto object detection challenge in the near future. I am certainly looking forward to seeing the results of the challenge to be posted around the time of the conference (September 2018).

Microsoft’s COCO Dataset

Microsoft is in this game also with their Common Objects in Context  (COCO) dataset. Containing ~200K images, it’s relatively small but what makes it stand out are its challenges that come associated with the additional features it provides for each image, for example:

  • object segmentation information rather than just bounding boxes of objects (see image below)
  • five textual captions per image such as “the a380 air bus ascends into the clouds” and “a plane flying through a cloudy blue sky”.

The first of these points is worth providing an example image of:

coco-eg

Notice how each object is segmented rather than outlined by a bounding box as is the case with ImageNet and Open Images examples? This object segmentation feature of the dataset makes for very interesting challenges because segmenting an object like this is many times more difficult than just drawing a rectangular box around it.

COCO challenges are also held annually. But each year’s challenge is slightly different. This year the challenge has four tracks:

  1. Object segmentation (as in the example image above)
  2. Panoptic segmentation task, which requires object and background scene segmentation, i.e. a task to segment the entire image rather than just the dominant objects in an image:
    panoptic-example
  3. Keypoint detection task, which involves simultaneously detecting people and localising their keypoints:
    keypoints-task-example
  4. DensePose task, which involves simultaneously detecting people and localising their dense keypoints (i.e. mapping all human pixels to a 3D surface of the human body):
    densepose-task-example

Very interesting, isn’t it? There is always something engaging taking place in the world of computer vision!

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The Early History of Computer Vision

Posted on by Zbigatron

As I’ve discussed in previous posts of mine, computer vision is growing faster than ever. In 2016, investments into US-based computer vision companies more than tripled since 2014 – from $100 million to $300 million. And it looks like this upward trend is going to continue worldwide, especially if you consider all the major acquisitions that were made in 2017 in the field, the biggest one being Intel’s purchase in March of Mobileye (a company specialising in computer vision-based collision prevention systems for autonomous cars) for a whopping $15 billion.

But today I want to take a look back rather than forward. I want to devote some time to present the early historical milestones that have led us to where we are now in computer vision.

This post, therefore, will focus on seminal developments in computer vision between the 60s and early 80s.

Larry Roberts – The Father of Computer Vision

Let’s start first with Lawrence Roberts. Ever heard of him? He calls himself the founder of the Internet. The case for giving him this title is strong considering that he was instrumental in the design and development of the ARPANET, which was the technical foundation of what you are surfing on now.

What is not well known is that he is also dubbed the father of computer vision in our community. In 1963 he published “Machine Perception Of Three-Dimensional Solids”, which started it all for us. There he discusses extracting 3D information about solid objects from 2D photographs of line drawings. He mentions things such as camera transformations, perspective effects, and “the rules and assumptions of depth perception” – things that we discuss to this very day.

Just take a look at this diagram from his original publication (which can be found here – and a more readable form can be found here):

Camera-transformation-roberts-PhD

Open up any book on image processing and you will see a similar diagram in there discussing the relationship between a camera and an object’s projection on a 2D plane.

Funnily enough, Lawrence’s Wikipedia page does not give a single utterance to his work in computer vision, which is all the more surprising considering that the publication I mentioned above was his PhD thesis! Crazy, isn’t it? If I find the time, I’ll go over and edit that article to give him the additional credit that he deserves.

The Summer Vision Project

Lawrence Roberts’ thesis was about analysing line drawings rather than images taken of the real world. Work in line drawings was to continue for a long time, especially after the following important incident of 1966.

You’ve probably all heard the stories from the 50s and 60s of scientists predicting a bright future within a generation for artificial intelligence. AI became an academic discipline and millions was pumped into research with the intention of developing a machine as intelligent as a human being within 25 years. But it didn’t take long before people realised just how hard creating a “thinking” machine was going to be.

Well, computer vision has its own place in this ambitious time of AI as well. People then thought that constructing a machine to mimic the human visual system was going to be an easy task on the road to finally building a robot with human-like intelligent behaviour.

In 1966, Seymour Papert organised “The Summer Vision Project” at the MIT. He assigned this project to Gerald Sussman who was to co-ordinate a small group of students to work on background/foreground segmentation of real-world images with a final goal of extracting non-overlapping objects from them.

Only in the past decade or so have we been able to obtain good results in a task such as this. So, those poor students really did now know what they were in for. (Note: I write about why image processing is such a hard task in another post of mine). I couldn’t find any information on exactly how much they were able to achieve over that summer but this will definitely be something I will try to find out if I ever get to visit the MIT in the United States.

Luckily enough, we also have access to the original memo of this project available to us – which is quite neat. It’s definitely a piece of history for us computer vision scientists. Take a look at the abstract (summary) of the project from the memo from 1966 (the full version can be found here):

summer-vision-project-abstract

Continued Work with Line Drawings

In the 1970s work continued with line drawings because real-world images were just too hard to handle at the time. To this extent, people were regularly looking into extracting 3D information about blocks from 2D images.

Line labelling was an interesting concept being looked at in this respect. The idea was to try to discern a shape in a line drawing by first attempting to annotate all the lines it was composed of accordingly. Line labels would include convex, concave, and occluded (boundary) lines. An example of a result from a line labelling algorithm can be seen below:

line-labelling-eg
Line labelling example with convex (+), concave (-), and occluded (<–) labels. (Image taken from here)

Two important people in the field of line labelling were David Huffman (“Impossible objects as nonsense sentences”. Machine Intelligence, 8:475-492, 1971) and Max Clowes (“On seeing things”. Artificial Intelligence, 2:79-116, 1971) who both published their line labelling algorithms independently in 1971.

In the genre of line labelling, interesting problems such as the one below were also looked at:

necker-illusion-marr

The image above was taken from a seminal book written by David Marr at the MIT entitled “Vision: A computational investigation into the human representation and processing of visual information”. It was finished around 1979 but posthumously published in 1982. In this book Marr proposes an important framework to image understanding that is used to this very day: the bottom-up approach. The bottom-up approach, as Marr suggests, uses low-level image processing algorithms as stepping-stones towards attaining high-level information.

(Now, for clarification, when we say “low-level” image processing, we mean tasks such as edge detection, corner detection, and motion detection (i.e. optical flow) that don’t directly give us any high-level information such as scene understanding and object detection/recognition.)

From that moment on, “low-level” image processing was given a prominent place in computer vision. It’s important to also note that Marr’s bottom-up framework is central to today’s deep learning systems (more on this in a future post).

Computer Vision Gathers Speed

So, with the bottom-up model approach to image understanding, important advances in low-level image processing began to be made. For example, the famous Lukas-Kanade optical flow algorithm, first published in 1981 (original paper available here), was developed. It is still so prominent today that it is a standard optical flow algorithm included in the OpenCV library. Likewise, the Canny edge detector, first published in 1986, is again widely used today and is also available in the OpenCV library.

The bottom line is, computer vision started to really gather speed in the late 80s. Mathematics and statistics began playing a more and more significant role and the increase in speed and memory capacity of machines helped things immensely also. Many more seminal algorithms followed this upward trend, including some famous face detection algorithms. But I won’t go into this here because I would like to talk about breakthrough CV algorithms in a future post.

Summary

In this post I looked at the early history of computer vision. I mentioned Lawrence Roberts’ PhD on things such as camera transformations, perspective effects, and “the rules and assumptions of depth perception”. He got the ball rolling for us all and is commonly regarded as the father of computer vision. The Summer Vision Project of 1966 was also an important event that taught us that computer vision, along with AI in general, is not an easy task at all. People, therefore, focused on line drawings until the 80s when Marr published his idea for a bottom-up framework for image understanding. Low-level image processing took off spurred on by advancements in the speed and memory capacity of machines and a stronger mathematical and statistical vigour. The late 80s and onwards saw tremendous developments in CV algorithm but I will talk more about this in a future post.

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feature-image-football-tabletop

Watch Football/Soccer on Your Tabletop using Virtual Reality

Posted on by Zbigatron

Well, it’s World Cup season now, isn’t it? Australia got eliminated this week so I’m feeling a bit depressed at the moment. However, seeing teams like Germany also not make it past the group stage makes me feel a little better (sorry German people reading this post :P).

But since the World Cup is on, it is only fitting that I write about something from the field of Computer Vision that is related to football. So, in this post I’m going to present to you quite an amazing paper I stumbled upon entitled “Soccer on Your Tabletop” (Rematas et al., CVPR 2018, pp. 4738-4747)

(Recall that CVPR is a world-class academic conference on computer vision. Anything published there is always worth reading.)

The goal of the paper is to present an algorithm that reconstructs a 3D representation of a football game from a single 2D video – just like you would find on YouTube. The 3D video could then be projected onto a tabletop (like a hologram) and viewed by everyone in the room from multiple angles. An interesting concept!

Usually something like this is obtained by having multiple cameras set up that can then work together to provide 3D information of the football pitch. But the idea here is to get all information from a single 2D video.

Here’s a clip of the entire project that has been released by the authors. Watch it!

Ah, you just have to love computer vision!

Let’s take a look at the (slightly simplified here) steps involved in the 2D -> 3D reconstruction process:

  1. Input frame: obtained from any 2D video of a football game (captured from stationary cameras).
  2. Camera calibration: this is performed using the football pitch line markings as guidance. The line markings provide excellent reference points to obtain the 2D plane of the football pitch from which players’ measurements can be deduced.
  3. Player detection, pose estimation, and tracking: this is done using already existing techniques. Specifically this paper is referenced from CVPR 2015 for detecting bounding boxes around players (top left image below), this paper from CVPR 2016 for estimation poses (top right image below), and a simple player tracking algorithm where you compare bounding boxes from adjacent frames and match them according to closest 2D Euclidean distance (bottom left image).

    football-on-tabletop-processing-steps
    (image taken from original publication)
  4. Player segmentation: the idea here is to highlight the entire contour of the player after performing the above steps (see bottom right image above). This is performed by taking each pixel and analysing its neighbouring pixels for similarities in colour and edge information until each player is extracted. (Several more steps are performed to fine-tune this process but I’ll skip over these).
  5. Player depth estimation and mesh generation. This is the tricky part. What the authors did is quite intuitive. To constrain the solution space to just football related poses, body shapes, and clothing, the authors created a training dataset from FIFA video games. Lol! What they found was that it was possible to intercept calls between the game engine and the GPU while playing the video game and then to extract depth maps from these intercepted calls. In doing so, they were able to train a deep neural network to extract depth maps from 2D videos. This trained network was then used on 2D YouTube videos. Absolutely brilliant!

    mesh-generation-football-tabletop
    (image obtained from project’s video)
  6. Scene reconstruction. Once player depth estimation and mesh information (which is 3D information) is obtained, the scene can then be reconstructed. What the authors ended up doing is to use Microsoft HoloLens (a mixed reality lens that enables you to see and interact with holograms in real life). So the football pitch on the tabletop you see in the image below isn’t real! Can you imagine watching a match like this around a table with your mates!? There is a catch with the project, however. It’s not good enough yet to reconstruct the ball, which means that at the moment all you can view in 3D are players running around chasing an invisible object 🙂 But that’s work in progress and the job and essence of research.

    hologram-football-tabletop
    (image obtained from project’s video)

Amazing, if you ask me! I can’t wait to see what the future holds for computer vision.

And believe it or not, code for this project is available online for you to play around with as much as you like. So, enjoy!

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Recent Controversies in Computer Vision – From Facebook to Uber

Posted on by Zbigatron

Computer vision is a fascinating area in which to work and perform research. And, as I’ve mentioned a few times already, it’s been a pleasure to witness its phenomenal growth, especially in the last few years. However, as with pretty much anything in the world, contention also plays a part in its existence.

In this post I would like to present 2 very recent events from the world of computer vision that have recently caused controversy:

  1. A judge’s ruling that Facebook must stand trial for its facial recognition software
  2. Uber’s autonomous car death of a pedestrian

Facebook and Facial Recognition

This is an event that has seemingly passed under the radar – at least for me it did. Probably because of the Facebook-Cambridge Analytica scandal that has been recently flooding the news and social discussions. But I think this is also an important event to mull over because it touches upon underlying issues associated with an important topic: facial recognition and privacy.

So, what has happened?

In 2015, Facebook was hit with a class action lawsuit (the original can be found here) by three residents from Chicago, Illinois. They are accusing Facebook of violating the state’s biometric privacy laws by the firm collecting and storing biometric data of each user’s face. This data is being stored without written notification. Moreover, it is not clear exactly what the data is to be used for, nor how long it will reside in storage, nor was there an opt-out option ever provided.

Facebook began to collect this data, as the lawsuit states, in a “purported attempt to make the process of tagging friends easier”.

cartoon-face-recognition

In other words, what Facebook is doing (yes, even now) is summarising the geometry of your face with certain parameters (e.g. distance between eyes, shape of chin, etc.). This data is then used to try to locate your face elsewhere to provide tag suggestions. But for this to be possible, the biometric data needs to be stored somewhere for it to be recalled when needed.

The Illinois residents are not happy that a firm is doing this without their knowledge or consent. Considering the Cambridge Analytica scandal, they kind of have a point, you would think? Who knows where this data could end up. They are suing for $75,000 and have requested a jury trial.

Anyway, Facebook protested over this lawsuit and asked that it be thrown out of court stating that the law in question does not cover its tagging suggestion feature. A year ago, a District Judge rejected Facebook’s appeal.

Facebook appealed again stating that proof of actual injury needs to be shown. Wow! As if violating privacy isn’t injurious enough!?

But on the 14th May, the same judge discarded (official ruling here) Facebook’s appeal:

[It’s up to a jury] to resolve the genuine factual disputes surrounding facial scanning and the recognition technology.

So, it looks like Facebook will be facing the jury on July 9th this year! Huge news, in my opinion. Even if any verdict will only pertain to the United States. There is still so much that needs to be done to protect our data but at least things seem to be finally moving in the right direction.

Uber’s Autonomous Car Death of Pedestrian

You probably heard on the news that on March 18th this year a woman was hit by an autonomous car owned by Uber in Arizona as she was crossing the road. She died in hospital shortly after the collision. This is believed to be the first ever fatality of a pedestrian in which an autonomous car was involved. There have been other deaths in the past (3 in total) but all of them have been of the driver.

uber-fatality-crash
(image taken from the US NTSB report)

3 weeks ago the US National Transportation Safety Board (NTSB) released its first report (short read) into this crash. It was only a preliminary report but it provides enough information to state that the self-driving system was at least partially at fault.

uber-fatality-car-image
(image taken from the US NTSB report)

The report gives the timeline of events: the pedestrian was detected about 6 seconds before impact but the system had trouble identifying it. It was first classified as an unknown object, then a vehicle, then a bicycle – but even then it couldn’t work out the object’s direction of travel. At 1.3 seconds before impact, the system realised that it needed to engage an emergency braking maneuver but this maneuver had been earlier disabled to prevent erratic vehicle behaviour on the roads. Moreover, the system was not designed to alert the driver in such situations. The driver began braking less than 1 second before impact but it was tragically too late.

Bottom line is, if the self-driving system had immediately recognised the object as a pedestrian walking directly into its path, it would have known that avoidance measures would have needed to be taken – well before the emergency braking maneuver was called to be engaged. This is a deficiency of the artificial intelligence implemented in the car’s system. 

No statement has been made with respect to who is legally at fault. I’m no expert but it seems like Uber will be given the all-clear: the pedestrian had hard drugs detected in her blood and was crossing in a non-crossing designated area of the road.

Nonetheless, this is a significant event for AI and computer vision (that plays a pivotal role in self-driving cars) because if these had performed better, the crash would have been avoided (as researchers have shown).

Big ethical questions are being taken seriously. For example, who will be held accountable if a fatal crash is deemed to be the fault of the autonomous car? The car manufacturer? The people behind the algorithms? One sole programmer who messed up a for-loop? Stanford scholars have been openly discussing the ethics behind autonomous cars for a long time (it’s an interesting read, if you have the time).

And what will be the future for autonomous cars in the aftermath of this event? Will their inevitable delivery into everyday use be pushed back?

Testing of autonomous cars has been halted by Uber in North America. Toyota has followed suit. And Chris Jones who leads the Autonomous Vehicle Analysis service at the technology analyst company Canalys, says that these events will set the industry back considerably:

It has put the industry back. It’s one step forward, two steps back when something like this happens… and it seriously undermines trust in the technology.

Furthermore, a former US Secretary of Transportation has deemed the crash a “wake up call to the entire [autonomous vehicle] industry and government to put a high priority on safety.”

But other news reports seem to indicate a different story.

Volvo, the make of car that Uber was driving in the fatal car crash, stated only last week that they expect a third of their cars sold to be autonomous by 2025. Other car manufacturers are making similar announcements. Two weeks ago General Motors and Fiat Chrysler unveiled self-driving deals with people like Google to push for a lead in the self-driving car market.

And Baidu (China’s Google, so to speak) is heavily invested in the game, too. Even Chris Jones is admitting that for them this is a race:

The Chinese companies involved in this are treating it as a race. And that’s worrying. Because a company like Baidu – the Google of China – has a very aggressive plan and will try to do things as fast as it can.

And when you have a race among large corporations, there isn’t much that is going to even slightly postpone anything. That’s been my experience in the industry anyway.

Summary

In this post I looked at 2 very recent events from the world of computer vision that have recently caused controversy.

The first was a judge’s ruling in the United States that Facebook must stand trial for its facial recognition software. Facebook is being accused of violating the Illinois’ biometric privacy laws by collecting and storing biometric data of each user’s face. This data is being stored without written notification. Moreover, it is not clear exactly what the data is being used for, nor how long it is going to reside in storage, nor was there an opt-out option ever provided.

The second event was the first recorded death of a pedestrian by an autonomous car in March of this year. A preliminary report was released by the US National Transportation Safety Board 3 weeks ago that states that AI is at least partially at fault for the crash. Debate over the ethical issues inherent to autonomous cars has heated up as a result but it seems as though the incident has not held up the race to bring self-driving cars onto our streets.

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