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Add vision capabilities to embedded systems

04 Dec 2012  | Jeff Bier

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One popular approach is the Lucas-Kanade method with image pyramid. The Lucas-Kanade method is a differential method of estimating optical flow; it is simple but has significant limitations. For example, it assumes constant illumination and constant motion in a small neighbourhood around the pixel position of interest. And, it is limited to very small velocity vectors (less than one pixel per frame).

Image pyramids are a technique to extend Lucas-Kanade to support faster motion. First, each original frame is sub-sampled to different degrees to create several pyramid levels. The Lucas-Kanade method is used at the top level (lowest resolution) yielding a coarse estimate, but supporting greater motion. Lucas-Kanade is then used again at lower levels (higher resolution) to refine the optical flow estimate. This is summarised in figure 4.

Figure 4: Lucas-Kanade optical flow algorithm with image pyramid. Used by permission of and Julien Marzat.

Algorithm example: Pedestrian detection
"Pedestrian detection" here refers to detecting the presence of people standing or walking, as illustrated in figure 5. Pedestrian detection might more aptly be called an "application" rather than an "algorithm." It is a complex problem requiring sophisticated algorithms.

Figure 5: Prototype pedestrian detection application implemented on a CPU and an FPGA.

Figure 6: Block diagram of proof-of-concept pedestrian detection application using an FPGA and a CPU.

In figure 6 we briefly summarise a prototype implementation of a stationary-camera pedestrian detection system implemented using a combination of a CPU and an FPGA.

In the figure, the Pre-processing block comprises operations such as scaling and noise reduction, intended to improve the quality of the image. The Image Analysis block incorporates motion detection, pixel statistics such as averages, colour information, edge information, etc. At this stage of processing, the image is divided into small blocks. The object segmentation step groups blocks having similar statistics and thus creates an object. The statistics used for this purpose are based on user defined features specified in the hardware configuration file.

The Identification and Meta Data generation block generates analysis results from the identified objects such as location, size, colour information, and statistical information. It puts the analysis results into a structured data format and transmits them to the CPU.

Finally, the On-screen Display block receives command information from the host and superimposes graphics on the video image for display.

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