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Technical Report TR 06-012 (May 2006) Department of Computer Science, UNC Chapel Hill

GPU-based Video Feature Tracking And Matching
Sudipta N. Sinha1 , Jan-Michael Frahm1 , Marc Pollefeys1 , Yakup Genc2
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Department of Computer Science, CB# 3175 Sitterson Hall, University of North Carolina at Chapel Hill, NC 27599 Real-time Vision and Modeling Department, Siemens Corporate Research, 755 College Road East, Princeton, NJ 08540

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 Introduction Extraction and matching of salient 2D feature points in video is important in many computer vision tasks like object detection, recognition, structure from motion and marker-less augmented reality. While certain sequential tasks like structure from motion for video [18] require online feature point tracking, others need features to be extracted and matched across frames separated in time (eg. wide-baseline stereo). The increasing programmability and computational power of the graphics processing unit (GPU) present in modern graphics hardware provides great scope for acceleration of computer vision algorithms which can be parallelized [3, 11, 12,14, 15,16, 17]. GPUs have been