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Enhancing surveillance apps with vision-based AI

Posted: 22 Aug 2014     Print Version  Bookmark and Share

Keywords:automated surveillance  artificial intelligence  embedded vision  DSP  SOCs 

TOF, on the other hand, is an active, higher-power sensor that generally offers more detail, but at higher cost and with a shorter operating range. Both approaches, along with structured light and other candidates, can be used for detection. But the optimum technology for a particular application can only be fully understood after prototyping (figure 6).

As new video compression standards such as H.265 become established, embedded vision surveillance systems will need to process even larger video formats (4k x 2k and beyond), which will compel designers to harness hardware processor combinations that may include CPUs, multi-core DSPs, FPGAs, GPUs, and dedicated accelerators.

Figure 6: Although the depth map generated by a TOF (time-of-flight) 3-D sensor is more dense than its stereo vision-created disparity map counterpart, with virtually no coverage "holes" and therefore greater accuracy in the TOF case, stereo vision systems tend to be lower power, lower cost and usable over longer distances.

Addressing often-contending embedded system complexity, cost, power, and performance requirements will likely lead to more distributed vision processing, whereby rich object and feature metadata extracted at the edge can be further processed, modelled, and shared in the cloud. The prospect of more advanced compute engines will enable state-of-the-art vision algorithms, including optical flow and machine learning.

Embedded vision technology has the potential to enable a wide range of electronic products, such as the surveillance systems discussed in this article, that are more intelligent and responsive than before, and thus more valuable to users. It can add helpful features to existing products. And it can provide significant new markets for hardware, software and semiconductor manufacturers.

About the authors
Brian Dipert is Editor-In-Chief of the Embedded Vision Alliance. He is also a Senior Analyst at BDTI (Berkeley Design Technology, Inc.), and Editor-In-Chief of InsideDSP, the company's online newsletter dedicated to digital signal processing technology. He has a B.S. degree in Electrical Engineering from Purdue University in West Lafayette, IN. His professional career began at Magnavox Electronics Systems in Fort Wayne, IN; Brian subsequently spent eight years at Intel Corporation in Folsom, CA. He then spent 14 years at EDN Magazine.

Jacob Jose is a Product Marketing Manager with Texas Instruments' IP Camera business. He joined Texas Instruments in 2001. He has engineering and business expertise in the imaging, video and analytics markets, and has worked at locations in China, Taiwan, South Korea, Japan, India, and the USA. He has a Bachelors degree in computer science and engineering from the National Institute of Technology at Calicut, India and is currently enrolled in the executive MBA program at Kellogg School of Business, Chicago, Ilinois.

Darnell Moore, Ph.D., is a Senior Member of the Technical Staff with Texas Instruments' Embedded Processing Systems Lab. As an expert in vision, video, imaging, and optimisation, his body of work includes Smart Analytics, a suite of vision applications that spawned TI's DMVA processor family, as well as advanced vision prototypes, such as TI's first stereo IP surveillance camera. He received a BSEE from Northwestern University and a Ph.D. from the Georgia Institute of Technology.

To download the PDF version of this article, click here.


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