Object tracking based on particle filter with discriminative features
Object tracking based on particle filter with discriminative features
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DOI：
10.1007/s11768-013-1088-0
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发表时间：
2013-01
期刊：
影响因子：
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通讯作者：
Yunji Zhao;Hailong Pei
中科院分区：
文献类型：
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作者：
Yunji Zhao;Hailong Pei
提出了一种具有在线特征选择机制的基于粒子滤波的视觉跟踪方法。在基于颜色的粒子滤波算法中，粒子的权重并不总是正确地代表重要性，这可能会导致基于粒子滤波的目标跟踪收敛到目标的局部区域。在我们提出的视觉跟踪方法中，使用Bhattacharyya距离和目标与背景的局部区分来定义粒子的权重，解决了存在的局部收敛问题。实验表明，该方法不仅适用于单目标跟踪过程，而且适用于多个相似目标跟踪过程。
This paper presents a particle filter-based visual tracking method with online feature selection mechanism. In color-based particle filter algorithm the weights of particles do not always represent the importance correctly, this may cause that the object tracking based on particle filter converge to a local region of the object. In our proposed visual tracking method, the Bhattacharyya distance and the local discrimination between the object and background are used to define the weights of the particles, which can solve the existing local convergence problem. Experiments demonstrates that the proposed method can work well not only in single object tracking processes but also in multiple similar objects tracking processes.