high performance computing on graphics processing units: hgpu.org

CONTEXT: Embedded platforms GPUs are reaching a level of performance comparable to desktop hardware. Therefore it becomes interesting to apply Computer Vision techniques to modern smartphones.The platform holds different challenges, as energy use and heat generation can be an issue depending on load distribution on the device. OBJECTIVES: We evaluate the viability of a feature detector and descriptor on the Xperia Z3. Specifically we evaluate the the pair basedon real-time execution, heat generation and performance. METHODS: We implement the feature detection and feature descriptor pair Harris-Hessian/FREAK for GPU execution using OpenCL,focusing on embedded platforms. We then study the heat generationof the application, its execution time and compare our method to twoother methods, FAST/BRISK and ORB, to evaluate the vision performance. RESULTS: Execution time data for the Xperia Z3 and desktop GeForceGTX660 is presented. Run time temperature values for a run ofnearly an hour are presented with correlating CPU and GPU activity. Images containing comparison data for BRISK, ORB and Harris-Hessian/FREAK is shown with performance data and discussion around notable aspects. CONCLUSION: Execution times on Xperia Z3 is deemed insufficientfor real-time applications while desktop execution shows that there isfuture potential. Heat generation is not a problem for the implementation. Implementation improvements are discussed to great lengthfor future work. Performance comparisons of Harris-Hessian/FREAK suggest that the solution is very vulnerable to rotation, but superiorin scale variant images. Generally appears suitable for near duplicatecomparisons, delivering much greater number of keypoints. Finally,insight to OpenCL application development on Android is given.