基于改进粒子滤波的视觉目标跟踪

针对目标跟踪算法在精度和鲁棒性上的要求,提出一种基于改进粒子滤波的视觉目标跟踪算法.首先,建立多种特征来描述目标外观模型,并对各特征分量的加权系数进行自适应调节;然后,利用分类重采样方法解决原始重采样方法中的粒子退化和匮乏问题;最后,提出一种新的模板更新机制,自适应选取运动模板或原始模板.实验结果表明,改进后的算法在具有挑战的跟踪视频序列上实验,具有良好的跟踪精度和鲁棒性,能够应对视频图像分辨率不高、目标转动变化、部分遮挡等复杂条件.

Visual object tracking based on improved particle filter

Aiming at the accuracy and robustness requirements of target tracking algorithm, we propose a visual target tracking algorithm based on improved particle filter. First, the target appearance model is described by establishing a variety of features, and the weighting coefficients of each feature component are adaptively adjusted. Then, we exploit the classification resampling method to solve the problem of particle degradation and scarcity in the original resampling method. Finally, a new template updating mechanism is proposed, which can adaptively select moving templates or original templates. The experimental results demonstrate that the improved algorithm has good tracking accuracy and robustness on the challenging tracking video sequences, and it can cope with the complex conditions such as low resolution of video images, rotation change of target, partial occlusion and so on.