一种新的层次粒子滤波的目标跟踪方法

Robust Object Tracking via Hierarchical Particle Filter

提出一种新的层次粒子滤波算法,选择局部区域特征点和颜色信息建立目标模型,引入粒子的二阶采样过程.算法通过粒子的一阶权重更新获得好的初始分布,二阶权重更新保证粒子的高置信度和高的采样效率,当粒子数目小于一定阈值时进行重要性重采样,利用仿射模型对目标区域精确定位及姿态修正.实验表明:改进算法将目标局部特征分布与目标颜色信息相结合,通过二阶采样过程,保证了局部特征跟踪的稳定性,解决了经典理论中误匹配导致的采样点发散问题,在目标部分遮挡情况下也可以完成实时目标跟踪.

A general hierarchical particle filtering framework for designing an optimal proposal distribution is proposed. The essential idea is to augment a second filter＇s estimate into the proposal distribution design. Based on this framework,a novel algorithm for robust and efficient visual tracking is given. The algorithm using hierarchical particle filtering procedures at each level and can efficiently extract high-confidence regions through video frames by exploiting the temporal consistency of region confidences. Comparisons with the mean shift tracker and a generic particle filter shows the advantages and limitations of the new approach.