基于低秩投影中稀疏误差矩阵分析的视觉跟踪

单目标跟踪是计算机视觉的重要组成部分,其鲁棒性一直受到目标遮挡、光照变化、目标尺度变化等因素的制约。针对这个问题,提出了基于低秩投影中稀疏误差矩阵分析的视觉跟踪算法。为了克服模板漂移对跟踪的影响,采用目标模板和候选目标的相似性关系动态选择目标模板的更新方式。在粒子滤波的框架下,利用鲁棒主成分分析和低秩投影原理求得候选目标的稀疏误差矩阵,根据稀疏误差矩阵的边缘信息和平滑度信息实现对下一帧目标的观测似然估计。在多个视频序列上的实验表明,算法具有较好的鲁棒性。

Visual tracking based on analysis of sparse error matrix in low rank projection

Single target tracking is an important part of computer vision, and its robustness is restricted by target occlusion, illumination variation and target scale variation. To deal with these problems, we propose a visual tracking algorithm based on the analysis of the sparse error matrix in low rank projection. In order to overcome the effect of model drifting, target templates are updated dynamically with the similarity between target templates and candidate targets. In the framework of particle filter, the sparse error matrix of candidate targets is obtained by using the theory of robust principal component analysis and low rank projection, and the observation likelihood estimation of the next frame is achieved according to edge and smoothness information. Experimental results on multiple video sequences show that this algorithm has better robustness performance than that of the state-of-the-art tracker.