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Embedded vision: Growing opportunities for FPGAs

Posted: 25 Jul 2012     Print Version  Bookmark and Share

Keywords:embedded vision  robotics  surveillance  FPGA 

As another example, black or white letters contained within a green highway sign might confuse the IP blocks' generic image-analysis functions, thereby incorrectly subdividing the sign into multiple-pixel subgroups (figure 2). The IP blocks might also incorrectly interpret other vehicles' rear driving or brake lights as cones or signs by confusing red with orange, depending on the quality and setup of the imaging sensor used for the application.

The BDTI-developed algorithms therefore served to further process the Xilinx-supplied metadata in an application-tailored manner. They knew, for example, what signs were supposed to look like (size, shape, colour, pattern, location within the frame and so on), and therefore were able to combine relevant pixel clusters into larger groups. Similarly, the algorithms determined when it was appropriate to discard seemingly close-in-colour pixel clusters that weren't signs, such as the aforementioned vehicle brake lights.

Figure 2: Second-level, application-tailored algorithms refined the metadata coming from the FPGA's video-analysis hardware circuits.

Phase 2: Pedestrian detection and tracking
In the first phase of this project, the camera was in motion but the objects (that is, signs) being recognised were stationary. In the second phase targeting security, on the other hand, the camera was stationary but objects (people, in this case) were not. Also, this time the video-analytics algorithms were unable to rely on predetermined colours, patterns or other object characteristics; people can wear a diversity of clothing, for example, and come in various shapes, skin tones and hair colours and styles (not to mention might wear head-obscuring hats, sunglasses and the like). And the software was additionally challenged with not only identifying and tracking people but also generating an alert when an individual traversed a digital "trip wire" and was consequently located in a particular region within the video frame (figure 3).

Figure 3: Pedestrian detection and tracking capabilities included a "trip wire" alarm that reported when an individual moved within a bordered portion of the video frame.


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