GPU-based Video Feature Tracking and Matching

Sudipta N. Sinha, Jan-michael Frahm, Marc Pollefeys, Yakup Genc
Department of Computer Science, CB# 3175 Sitterson Hall, University of North Carolina at Chapel Hill, NC 27599
In Workshop on Edge Computing Using New Commodity Architectures (2006)


   title={GPU-based video feature tracking and matching},

   author={Sinha, S.N. and Frahm, J.M. and Pollefeys, M. and Genc, Y.},

   booktitle={EDGE, Workshop on Edge Computing Using New Commodity Architectures},





Abstract This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware, the CPU is freed up to run other computations in parallel. Our GPU-based KLT implementation tracks about a thousand features in real-time at 30 Hz on 1024 × 768 resolution video which is a 20 times improvement over the CPU. It works on both ATI and NVIDIA graphics cards. The GPU-based SIFT implementation works on NVIDIA cards and extracts about 800 features from 640 × 480 video at 10Hz which is approximately 10 times faster than an optimized CPU implementation. 1
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