看电视?其实电视也想看你!

上网时间: 2013年12月12日? 作者:Junko Yoshida? 我来评论 【字号: ? ?小】

关键字:机器视觉? 计算机视觉? 嵌入式视觉技术?

Embedded Vision: Who's Watching Whom & Why

Junko Yoshida, Chief International Correspondent

TOKYO — Imaging technology is no longer just about the never-ending megapixel race among CMOS image sensors. As market focus shifts to "vision" processing, the industry has drawn a new battle line -- over how fast and how accurately a processor can capture, dissect, and interpret data in a manner comprehensible to an embedded system.

In short, the whole concept of who's watching whom has flipped.

In the embedded vision world, what matters is not so much you, the photographer, who wants to take better photos; instead, the technology now exists to cater to embedded systems that need to watch you, recognize who you are, analyze your behavior, and process data they think you need.

You might call this just the plain reality of technology progress in machine vision or computer vision. Maybe so. But I confess that some of the embedded vision plots hatched by marketers today are disturbing enough to make me cringe.

None of this stuff, of course, is more worrisome than the NSA's electronic spying programs. But the very notion of a bunch of sensors physically watching me -- solely to make a commercial gain at my expense -- gives me, at least, a slight case of the willies. At worst, it's a reminder of the increasingly Orwellian society we already live in.

Over a cup of coffee in Tokyo, I recently sat down with Tom Wilson, vice president of business development at CogniVue, a Quebec-based embedded vision technology developer. Wilson tried to convince me that automotive isn't the only market being targeted by vision processing technology developers like CogniVue.

Here are a few examples he shared with me -- in terms of what comes next with embedded vision:

? Drive a car on a deserted road in the dark. Street lamps -- normally switched off -- light up the road just in front of your car, as you move forward. As soon as they sense your car is leaving, they go off. (Yeah, I know: an evening's drive through The Twilight Zone.)

? Walk in front of a digital sign -- a gigantic electronic display in a public space. The sign, even before you notice it, recognizes your gender and age, then quickly changes the ad message -- to fit your demographic profile -- as you look at it. (Yeah, I know: shades of Minority Report.)

? Smartphones that can recognize your hand gestures, or that can do face recognitions to help you tag images (by informing you who you are seeing, and whose pictures you are taking, and even uploading to social networks.)

? A set-top box embedded with eyes in your living room identifies who is watching what program. It sends the information to a backend server, triggering a digital product placement in a TV program. (Right. Saw that in Fahrenheit 451.)

Among these examples, what ticked me off was the last item about a set-top box with eyes. Of course, for someone who's known Kinect (a motion sensing input device by Microsoft for the Xbox 360 video game console and Windows PCs), I probably shouldn't have been so surprised. But I needed further clarification over what it exactly does.

"Say you are watching Friends. The set-top box knows you're watching it and you actually like Pepsi instead of Coke," explained CogniVue’s Wilson. The backend server, then, can digitally insert a Pepsi can, replacing a Coke, in Monica's living room.

Click here to watch Mirriad's video explaining how its services work.

(Source: Mirrad)

Wilson pointed out that Mirriad, a developer of ad platforms, is one company working on such a project. "The plan is to couple this type of ad insertion with viewer preference," he explained. In fact, a set-top box with eyes isn't such a far-fetched idea. Mirriad recently signed a deal with Pace, a set-top box vendor, to trial this in the UK, according to Wilson.

While explaining the digital product placement scheme, Wilson joked that this is partly why he doesn't own a TV. But he made sure that I understood the far-reaching ramifications of embedded vision applications and how the competition among embedded vision IP vendors -- both software and hardware -- has been escalating in recent years.

CogniVue, Mobileye, CEVA, and Tensilica (now a part of Cadence) are just a few examples of IP companies enabling embedded vision technologies. The newest member to join the fray is Imagination Technology, which announced its PowerVR Raptor ISP (image signal processing) architecture Monday.

Leading chip companies such as Freescale, Texas Instruments, and STMicroelectronics are also rolling out purpose-built vision processors -- often taking advantage of their partnership/licensing deals with embedded vision IP vendors.

For the time being, though, automotive is the primary market for all these vision processors, since embedded vision is playing a key role in Advanced Driver Assist System (ADAS). Carmakers are banking on ADAS, advocating safety features such as lane departure warnings, collision mitigation, self-parking, and blind-spot notification.

According to IHS, a market research firm, revenue in 2013 for special-purpose computer vision processors used in under-the-hood automotive applications is forecast to reach $151 million, up from $137 million last year and from $126 million in 2011.

Hard problems to solve

I should, however, note that the industry is still scratching only the surface of the embedded vision future.

"Vision processing still remains as a very hard problem to solve,"Jeff Bier, founder of the Embedded Vision Alliance, once told EE Times, "despite the number of man-years spent developing a host of embedded vision algorithms."

CogniVue’s Wilson agreed. Processing a huge amount of real-time data demands intense compute power. To do a 3D sensor map in a robust manner, especially in a low-power consumer device, is especially tough, he added.

Asked why a 3D sensor map, he described it as "essential" to solve fundamental limitations in 2D computer vision. He noted that 2D, for example, has problems with segmentation (separating foreground from background), illumination (for face recognition), relative position (placing objects in the scene), and occlusion (hands in front of the face). Noting that different approaches for 3D sensing are fraught with tradeoffs, Wilson said that CogniVue is currently working on an algorithmic way to efficiently compute disparity maps for low-cost 3D sensor vision.

Designing hardware that can efficiently run different vision algorithms is a huge challenge for system designers. Options for system vendors looking for imaging/video processing solutions range from keeping it all in the CPU to offloading imaging to the GPU, or adding hardwired logic dedicated to imaging functions.

With the world's GPU IP core leader Imagination entering the vision market, the race among IP vendors and chip suppliers has only gotten even more intense.

There is no question that it's going to be a "Brave New World."


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