影像技术已经不再只是CMOS影像传感器之间永无止尽的的像素竞赛，随着市场焦点转向“视觉(Vision)”处理，产业界的新战线已经来到了处理器能如何快速、精准地以一种能理解嵌入式系统的方式，撷取、分析并诠释资料。
简单来说，“谁在看谁”的概念已经出现反转。在嵌入式视觉的世界，关键主体不是想拍出更加照片的你或是摄影师们，为嵌入式系统而开发的技术要“看”的对象是你、它们要识别你是谁、分析你的行为，然后处理那些它们认为你会需要的资料。
可能在你的认知里，这些技术就是“机器视觉”或是“计算机视觉”；也许没错，但坦白说，目前有一些利用嵌入式视觉技术来执行的市场行销策略，令人颇感不安。当然，这类市场营销手法不至于像是美国国家安全局(NSA)的电子监听那么恐怖，但该种技术基本上就是有一堆传感器在监视我，目标就是要赚我的钱──这实在 让我觉得毛骨悚然。
我最近在日本东京与一家美国嵌入式视觉技术开发商CogniVue 的业务发展副总裁Tom Wilson见面；他告诉我，像CogniVue那样的视觉处理技术开发业者，目标市场不会只是汽车应用。以下是Wilson所分享的几个嵌入式视觉技术应用案例：
●当驾驶人在黑暗荒凉的道路上开车，(平常是关闭的)路灯会在汽车行进到前方时开启，当它们感应到汽车已经离开就会再度关闭。
●当你从一个数字看板──也就是公共场所的大型电子显示器──前方走过，那个屏幕甚至会在你注意到它之前，就可以辨别出你的性别与年龄，然后快速变换所显示的广告消息，以迎合你所属的人口族群喜好、吸引你的目光。
●智能手机可识别你的手势，或者是支持人脸识别协助你标记影像(通知你那个你正在看的人是谁、或是你所拍的照片里有那些人，还会上传到社群网站)。
●有一双“眼睛”的机顶盒，会观察你家客厅、识别出谁在看哪些电视节目，然后将那些信息传送到后端服务器，在你正在看的节目里置入你会有兴趣的数字产品广告。
在以上几个案例里面，最吸引我注意的就是最后那种“有眼睛的机顶盒”；当然，因为知道微软(Microsoft) Xbox 360游戏机的体感识别装置 Kinect ，这种技术或许不那么令人惊讶，但我就是很想进一步了解它的运作原理。
对此Wilson的解释是：“那就是说，如果你正在看美剧《Friends》，那台机顶盒会知道是你在看，然后知道你喜欢的是可口可乐而不是百事可乐。”于是后端的服务器能以数字化的方式，把影集中人物正在使用的植入性营销产品，换成你喜欢的那种。
Wilson 指出，有一家广告平台开发商Mirriad，就是专门提供这样的解决方案：“他们的方案就是将置入性广告类型与观众的喜好搭配。”根据他的说法，该种“有 眼睛的机顶盒”并不是一个牵强的概念，Mirriad这家公司最近已经与机顶盒供货商Pace签署合作协议，要在英国试用这种解决方案。
本文授权编译自EE Times，版权所有，谢绝转载
第2页：汽车应用是各种视觉处理器的主战场
第3页：仍有待克服的市场障碍
相关阅读：
• DLP嵌入式平台打开机器视觉开发大门
• 你的智能手机或许正用摄像头监控你……
• [图文报道]让人眼花缭乱的嵌入式视觉应用JbDesmc
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在解释何谓数字广告植入性营销方案时，Wilson开玩笑地表示，这就是他家没有电视的原因之一；但他也让我理解了那些嵌入式视觉应用带来的深远影响，以及嵌入式视觉IP (包括软件与硬件)供货商之间的竞争，在近几年来有越演越烈的趋势。
CogniVue、 Mobileye、 CEVA 与 Tensilica (现已收归Cadence 旗下)是目前市场上可提供嵌入式视觉技术的几家IP供货商，Imagination Technology 最近也藉由发表PowerVR Raptor ISP (image signal processing，影像信号处理)技术成为该领域的新竞争者。
其它芯片大厂包括Freescale、TI与ST也有推出特殊应用视觉处理器产品，但通常是与业界伙伴合作，或是与嵌入式视觉IP供货商签属授权协议。
目 前汽车应用是各种视觉处理器的主战场，因为嵌入式视觉在先进驾驶人辅助系统(ADAS)内扮演要角；汽车厂商正指望ADAS带来新商机，大力宣传该系统可 提供的各种安全功能如车道偏离警告、撞击缓解(collision mitigation)、自动停车，以及盲点提醒等等。
根据市场研究机构IHS的估计，特殊应用视觉处理器在汽车市场的应用规模，2013年可达到1.51亿美元；该数字在 2012年为1.37亿美元，在2011年则为1.26亿美元。
JbDesmc
本文授权编译自EE Times，版权所有，谢绝转载
第3页：仍有待克服的市场障碍
相关阅读：
• DLP嵌入式平台打开机器视觉开发大门
• 你的智能手机或许正用摄像头监控你……
• [图文报道]让人眼花缭乱的嵌入式视觉应用JbDesmc
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仍有待克服的市场障碍
不 过目前产业界其实仍只看到嵌入式视觉的表面；如嵌入式视觉联盟(Embedded Vision Alliance)创办人Jeff Bier先前接受EETimes 美国版访问时所言：“视觉处理仍有许多非常困难的问题有待解决，就算人们花费大量的时间开发了一系列嵌入式视觉算法。”
CogniVue 的Wilson也同意以上看法，他指出，要处理大量的实时数据需要非常密集的运算性能，而要以一个强健的方式架构出“3D传感器映像图(3D sensor map)”，特别是在诉求低功耗的消费性电子装置中，更是艰难任务。Wilson解释，所谓的”3D传感器映像图”是解决目前2D计算机视觉基本限制的关键。
举例来说，2D技术在影像分割(segmentation，也就是分开背景与前景)、照度(illumination， 支持人脸识别)、相对定位(relative position，辨别画面中物体相对位置)，以及遮蔽(occlusion，识别人脸前方的手)等方面有问题，而不同3D感测方案都面临性能上的折衷。 Wilson表示，CogniVue现在正试图透过算法解决映像图问题，以催生低成本3D传感器视觉方案。
对系统设计工程师来说，要设计出能有效执行不同视觉算法的硬件，是很大的挑战；那些正在寻找影像/视频处理解决方案的系统供货商，可选择把所有任务留在CPU里面、将影像任务交给GPU，或是添加专门处理影像的硬件逻辑。
随着像Imagination这样的主流GPU核心供货商涉足视觉市场，可预见相关IP供货商与芯片厂商的竞争将更加激烈。而我们也可以预期，未来将会有各种各样让人惊艳的嵌入式视觉解决方案出现在日常生活中…拭目以待那个“美丽新世界“吧！
本文授权编译自EE Times，版权所有，谢绝转载
编译：Judith Cheng
参考英文原文：Embedded Vision: Who's Watching Whom & Why，by Junko Yoshida
相关阅读：
• DLP嵌入式平台打开机器视觉开发大门
• 你的智能手机或许正用摄像头监控你……
• [图文报道]让人眼花缭乱的嵌入式视觉应用JbDesmc
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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."
