基于多表观特征子模型更新的鲁棒视觉跟踪

Robust Visual Tracking Based on Sub-model Updating of Multiple Apparent Features

在视觉跟踪中,传统模型更新算法在遮挡、光照变化及自身旋转等情况下通常存在鲁棒性较差的问题.为改善该性能,提出一种对多表观特征相应子模型进行选择性更新的鲁棒视觉跟踪算法.该算法首先建立候选子模型库,然后通过三个互补特征融合的粒子滤波跟踪确定当前帧目标位置和信息,最后将当前帧三种特征直方图信息与候选库中各子模型分别计算加权相似度,更新候选库后与阈值比较,判断是否更新当前子模型.实验结果表明:本文算法能够对特征相应子模型进行有效的选择性更新,与对比算法比较,在多种复杂变化的跟踪条件下,总体上能够具有更好的跟踪鲁棒性.

In computer vision tracking,the traditional model updating has poor robustness in solving the problem of occlusion, illumination change and self rotation. To improve these problems,this study proposes a new visual object tracking method. The algorithm firstly builds a candidate update sub-model lihrary. Secondly,it determines the position and informa- tion of the current target by fusing the three complementary features of the tracking based on Particle Filter. Finally, the algorithm divides the three characteristic histogram of the target and the candidate model library to calculate the similarity of the reliability weights, then determines whether the candidate sub-model library and current sub-model can be updated. Results show that the algorithm can effectively select to update the sub-model. Rather than the contrast algorithms, our method can achieve a better tracking accuracy to deal with the situation of occlusion,illumination change and self rotation. The proposed method updates the target model effectively and keeps the good robustness under various tracking scenarios.