GPU-accelerated computation for robust motion tracking using the CUDA framework

Jing Huang, Sean P. Ponce, Seung In Park, Yong Cao, Francis Quek
Center of Human Computer Interaction, Virginia Polytechnic Institute and State University, Blacksburg, 24060, USA
5th International Conference on Visual Information Engineering, 2008. VIE 2008


   title={GPU-accelerated computation for robust motion tracking using the CUDA framework},

   author={Huang, J. and Ponce, S.P. and Park, S.I. and Cao, Y. and Quek, F.},

   booktitle={Visual Information Engineering, 2008. VIE 2008. 5th International Conference on},






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In this paper, we discuss our implementation of a graphics hardware acceleration of the Vector Coherence Mapping vision processing algorithm. Using this algorithm as our test case, we discuss our optimization strategy for various vision processing operations using NVIDIA’s new CUDA programming framework. We also demonstrate how flexibly and readily vision processing algorithms can be mapped onto massively parallelized GPU architecture. Our results and analysis show the GPU implementation exhibits a performance gain of more than 40-fold of speedup over state-of-art CPU implementation of VCM algorithm.
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