With the emergence of increasingly powerful processors, image sensors, memory and other semiconductor devices and related algorithms, computer vision capabilities can be implemented in a variety of embedded systems to analyze the surrounding environment through video input. Products such as Microsoft's Kinect game controller and Mobileye's driver assistance system place great emphasis on the potential of embedded vision technology. As a result, many embedded system designers are beginning to think about how to implement embedded vision capabilities. This article examines the opportunities for embedded vision, compares the various processor choices for implementing this technology, and introduces industry alliances that help engineers adopt visual capabilities in their designs.
The term "embedded vision" refers to the use of computer vision technology in embedded systems. In other words, "embedded vision" refers to an embedded system that extracts the meaning behind it from visual input. Similar to the popularity of wireless communication technology in the past 10 years, embedded vision technology is expected to be widely used in the next 10 years.
Clearly, embedded vision technology can bring tremendous value to a variety of applications. Two examples are Mobileye's vision-based assisted driving system to help prevent vehicle accidents; and MG International's pool safety system designed to prevent swimming drowning. There are also some pure geeks invented, the IntellectualVentures laser killer gun, designed to prevent people from contracting malaria.
Just as high-speed wireless interconnects start with high-cost novelty technologies, embedded vision technology has so far only been used in complex and expensive systems, such as surgical robots for hair transplants, quality control inspection systems for manufacturing, and so on.
The development of digital integrated circuits is a key factor in the development of high-speed wireless technology from novelty to mainstream. High-speed wireless technology has become the mainstream technology in the market when the chip speed is fast enough, the cost is low enough, and the energy efficiency is high enough. Today, people can buy broadband wireless modems for less than $100.
Similarly, advances in digital chip technology have paved the way for high-volume applications of embedded vision. Similar to wireless communications, embedded vision requires a lot of processing power—especially, more and more applications use high-resolution cameras and start using multiple cameras. It is a big challenge to achieve this type of processing power at a low enough cost to facilitate high volume applications. The fact is that embedded vision applications require a lot of programmability, further increasing the difficulty of this challenge. In wireless applications, the standard means that the baseband algorithm between different mobile phones does not change much. In contrast, in embedded vision applications, it is possible to get better results through unique algorithms. More valuable features.
With embedded vision, the industry has entered a “virtuous cycle†that is characteristic of many other digital signal processing (DSP) applications. Although there are few chips specifically for embedded vision applications, these applications are increasingly using high-performance, cost-effective processing chips for other applications, including digital signal processors, CPUs, FPGAs, and GPU, etc. The programmability of these chips per unit cost and unit power is increasing, thus enabling the implementation of high-volume embedded vision products. These high-volume applications have also attracted more attention from silicon providers, who will provide better performance, higher efficiency and programmable processing power.
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