一种基于多特征融合的视频目标跟踪方法

Robust video object tracking algorithm based on multi-feature fusion

针对复杂场景下多特征跟踪算法适应性不强的问题,提出一种多特征有效融合和更新的目标跟踪方法.该方法首先在粒子滤波框架下采用加权融合的方式对目标进行多特征观测和相似性度量,通过分析粒子的空间集中度和权值分布建立一种有效的融合系数计算方法,使融合结果更加准确可靠.然后选取可信度高的特征检测遮挡,并动态调整目标模型的更新速度,以降低算法受目标变化和部分遮挡的影响.实验证明该方法对复杂的跟踪场景具有更强的鲁棒性,并适用于目标被遮挡时的跟踪.

Object tracking using multiple features has poor performance under complex scenes and when occlusion occurs. Therefore, an algorithm for fusing multiple features adaptively in the particle filter tracking framework is proposed, The tracked object is represented by the fusion of all features under linear weighting, and a new method to estimate the fusion coefficient is also proposed according to the weight distribution of all particles as well as their spatial concentrations, thus improving the reliability of multi features fusion. Besides, a dynamic updating strategy is used to adjust the update speed of each feature template adaptively, thus alleviating the affection of object deformation. According to the confidence of each feature, an occlusion handling strategy is invoked to decrease the influence of partial occlusion. Analysis and experiment show that the proposed method is more robust under complex scenes, and is applicable in the presence of occlusions.