A computationally efficient importance sampling tracking algorithm

被引：1

作者：

Farah, Rana ^{[1
]}

Gan, Qifeng ^{[1
]}

Langlois, J. M. Pierre ^{[1
]}

Bilodeau, Guillaume-Alexandre ^{[1
]}

Savaria, Yvon ^{[2
]}

机构：

[1] Ecole Polytech, Comp & Software Engn Dept, Montreal, PQ H3C 3A7, Canada

[2] Ecole Polytech, Dept Elect Engn, Montreal, PQ H3C 3A7, Canada

来源：

MACHINE VISION AND APPLICATIONS | 2014年 / 25卷 / 07期

关键词：

Video tracking; CONDENSATION algorithm; Particle filter; PARTICLE FILTER;

D O I：

10.1007/s00138-014-0630-5

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 proposes a computationally efficient importance sampling algorithm applicable to computer vision tracking. The algorithm is based on the CONDENSATION algorithm, but it avoids expensive operations that are costly in real-time embedded systems. It also includes a method that reduces the number of particles during execution and a new resampling scheme. Our experiments demonstrate that the proposed algorithm is as accurate as the CONDENSATION algorithm. Depending on the processed sequence, the acceleration with respect to CONDENSATION can reach 7 for 50 particles, 12 for 100 particles and 58 for 200 particles.

引用

页码：1761 / 1777

页数：17

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