关键词: 目标跟踪, 稀疏解, 时空上下文算法, 模板更新

Abstract: Aiming at the problem of failure of tracking target caused by strong occlusion, we proposed an improved spatiotemporal context algorithm based on dictionary learning. Firstly, foreground dictionary and context dictionary were constructed in the target area and the context area, and then we used the characteristics of the sparse solution to give higher weight to the feature of the extraction target, the feature with good effect was used to update the template and construct a new conditional probability. The experimental results show that the tracking success rate of spatiotemporal context algorithm is 195% in the data set with severe occlusion, and that of the improved algorithm is 945%. The improved algorithm can effectively resist the occlusion problem and track stably in the case of strong occlusion.

Key words: target tracking, sparse solution, spatiotemporal context algorithm, template update