An improved target tracking algorithm based on spatio-temporal context under occlusions

Abstract

Target tracking is a popular but challenging problem in computer vision field. Due to many aggravating factors such as position transformation, illumination, occlusion, it is difficult to achieve robust target tracking. According to the above constraints, an improved target tracking algorithm based on spatio-temporal context (STC) under occlusions is proposed. On the basis of STC, the proposed method introduces a novel mechanism for dealing with occlusion, the scale update, and the learning rate update to reduce the error update of the model and restrain error accumulation. As a consequence, the tracking performance can be improved efficiently. Extensive experimental results show that our algorithm outperforms the original STC algorithm and some other state-of-the-art algorithms.