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Abstract:
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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 . |