Evaluation of a mobile computing platform for image processing
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Many modern mobile applications, such as Unmanned Aerial Vehicles (UAVs), require sophisticated processing capability with low power consumption in a small form factor. UAVs, for example, may require a platform capable of controlling a camera, performing digital signal processing techniques on the pictures to detect faces or motion, and guiding the vehicle based on decisions made from the processed data. Additionally, since the vehicle is mobile and aerial, its effectiveness is heavily dependent on the size and power consumption of the platform. In this report, we explore this set of requirements and how well they are met with a Texas Instruments OMAP SoC on a BeagleBoard. Specifically, we report on the computational performance and power drawn by the OMAP General Purpose Processor (GPP) when performing a facial detection algorithm with OpenCV. We also analyze the performance enhancement possible by offloading the facial detection algorithm to the OMAP DSP coprocessor. In summary we find that the Beagleboard would be an appropriate platform for a simpler UAV capable of pre-processing still images taken every few seconds, but not for processing video data real-time. We conclude by describing other applications that are suitable for the Beagleboard.