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Add vision capabilities to embedded systems

04 Dec 2012  | Jeff Bier

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We use the term "embedded vision" to refer to the utilisation of computer vision technology in embedded systems. Stated another way, "embedded vision" refers to embedded systems that extract meaning from visual inputs. Similar to the way that wireless communication has become pervasive over the past 10 years, we believe that embedded vision technology will be very widely deployed in the next 10 years.

It's clear that embedded vision technology can bring huge value to a vast range of applications. Two examples are Mobileye's vision-based driver assistance systems, intended to help prevent motor vehicle accidents, and MG International's swimming pool safety system, which helps prevent swimmers from drowning. And for sheer geek appeal, it's hard to beat Intellectual Ventures' laser mosquito zapper, designed to prevent people from contracting malaria.

Just as high-speed wireless connectivity began as an exotic, costly technology, embedded vision technology has so far typically been found in complex, expensive systems, such as a surgical robot for hair transplantation and quality control inspection systems for manufacturing.

Advances in digital integrated circuits were critical in enabling high-speed wireless technology to evolve from exotic to mainstream. When chips got fast enough, inexpensive enough, and energy efficient enough, high-speed wireless became a mass-market technology. Today one can buy a broadband wireless modem for under $100.

Similarly, advances in digital chips are now paving the way for the proliferation of embedded vision into high-volume applications. Like wireless communication, embedded vision requires lots of processing power—particularly as applications increasingly adopt high-resolution cameras and make use of multiple cameras. Providing that processing power at a cost low enough to enable mass adoption is a big challenge. This challenge is multiplied by the fact that embedded vision applications require a high degree of programmability. In contrast to wireless applications where standards mean that, for example, algorithms don't vary dramatically from one cell phone handset to another, in embedded vision applications there are great opportunities to get better results—and enable valuable features—through unique algorithms.

With embedded vision, we believe that the industry is entering a "virtuous circle" of the sort that has characterized many other digital signal processing application domains. Although there are few chips dedicated to embedded vision applications today, these applications are increasingly adopting high-performance, cost-effective processing chips developed for other applications, including DSPs, CPUs, FPGAs, and GPUs. As these chips continue to deliver more programmable performance per dollar and per watt, they will enable the creation of more high-volume embedded vision products. Those high-volume applications, in turn, will attract more attention from silicon providers, who will deliver even better performance, efficiency, and programmability.

Algorithms
Algorithms are the essence of embedded vision. Through algorithms, visual input in the form of raw video or images is transformed into meaningful information that can be acted upon.

Computer vision has been the subject of vibrant academic research for decades, and that research has yielded a deep reservoir of algorithms. For many system designers seeking to implement vision capabilities, the challenge at the algorithm level will not be inventing new algorithms, but rather selecting the best existing algorithms for the task at hand, and refining or tuning them to the specific requirements and conditions of that task.

The algorithms that are applicable depend on the nature of the vision processing being performed. Vision applications are generally constructed from a pipelined sequence of algorithms, as shown in figure 1. Typically, the initial stages are concerned with improving the quality of the image. For example, this may include correcting geometric distortion created by imperfect lenses, enhancing contrast, and stabilising images to compensate for undesired movement of the camera.

Figure 1: A typical embedded vision algorithm pipeline.


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