Feature tracking and matching in video using programmable graphics hardware

被引：125
|
作者：
Sinha, Sudipta N. [1 ]
Frahm, Jan-Michael [1 ]
Pollefeys, Marc [1 ]
Genc, Yakup [2 ]
机构：
[1] Univ N Carolina, Dept Comp Sci, Chapel Hill, NC 27599 USA
[2] Siemens Corp Res, Real Time Vis & Modeling Dept, Princeton, NJ 08540 USA
来源：
MACHINE VISION AND APPLICATIONS | 2011年 / 22卷 / 01期
关键词：
Visual tracking; Vehicle tracking; Video surveillance; Visual inspection; Vision system; Robot navigation;
D O I：
10.1007/s00138-007-0105-z
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 1,024 x 768 resolution video which is a 20 times improvement over the CPU. The GPU-based SIFT implementation extracts about 800 features from 640 x 480 video at 10 Hz which is approximately 10 times faster than an optimized CPU implementation.
引用
收藏
页码：207 / 217
页数：11
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