基于子空间自适应学习的粒子滤波跟踪算法

计算机应用 ›› 2014, Vol. 34 ›› Issue (12): 3526-3530.

基于子空间自适应学习的粒子滤波跟踪算法

吴桐¹,²,王玲¹,何凡²

1. 湖南大学电气与信息工程学院,长沙 410082
2. 中国洛阳电子装备试验中心,河南洛阳 471000

收稿日期:2014-06-11 修回日期:2014-08-11 发布日期:2014-12-31 出版日期:2014-12-01
通讯作者: 吴桐
作者简介:吴桐(1987-),男,江苏如东人,助理工程师,硕士研究生, 主要研究方向:计算机视觉、机器学习；王玲(1962-),女,湖南长沙人,教授,博士生导师,博士,主要研究方向:计算机视觉、机器学习；何凡(1987-),男,安徽合肥人,助理工程师,主要研究方向:机器学习、电子对抗效能评估。

Particle filter tracking algorithm based on adaptive subspace learning

WU Tong¹,²,WANG Ling¹,HE Fan²

1. College of Electrical and Information Engineering, Hunan University, Changsha Hunan 410082, China
2. Luoyang Electronic Equipment Examination Center of China, Luoyang Henan 471003, China;

Received:2014-06-11 Revised:2014-08-11 Online:2014-12-31 Published:2014-12-01
Contact: WU Tong

摘要/Abstract

摘要：

为了提高目标外观迅速变化时视觉跟踪算法的鲁棒性,提出了一种基于自适应子空间学习的粒子滤波跟踪算法。在粒子滤波构架下,建立状态判决机制,根据判决结果并结合主成分分析(PCA)子空间与正交子空间的特点,选择合适的学习方法。这样既能准确、稳定地学习到目标的低维子空间,又能迅速地学习到目标外观变化的趋势。同时,加入鲁棒估计技术处理遮挡问题,避免了对目标状态估计的影响。实验结果表明,该算法在光照变化、姿态变化、遮挡的情况下,均具有较强的鲁棒性。

Abstract:

In order to improve the robustness of visual tracking algorithm when the target appearance changes rapidly, a particle filter tracking algorithm based on adaptive subspace learning was presented in this paper. In the particle filter framework, this paper established a state decision mechanism, chose the appropriate learning method by combining the verdict and the characteristics of the Principal Component Analysis (PCA) subspace and orthogonal subspace. It not only can accurately, stably learn target in low dimensional subspace, but also can quickly learn the change trend of the target appearance. For the occlusion problem, robust estimation techniques were added to avoid the impact of the target state estimation. The experimental results show that the algorithm has strong robustness in the case of illumination change, posture change, and occlusion.

中图分类号:

TP391

吴桐王玲何凡. 基于子空间自适应学习的粒子滤波跟踪算法[J]. 计算机应用, 2014, 34(12): 3526-3530.

WU Tong WANG Ling HE Fan. Particle filter tracking algorithm based on adaptive subspace learning[J]. Journal of Computer Applications, 2014, 34(12): 3526-3530.

参考文献

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