Visual Tracking Combined Least Soft-Threshold Squares with Haar-like Feature Matching

The object tracking method based on least soft-threshold squares deals with the appearance change and outlier of video well. However, when the object subspace is influenced by interference such as posture change or occlusion, the tracking robustness is not completely effective. To solve this problem, this study proposes an online object tracking algorithm which combines least soft-threshold squares with compressed Haar-like feature matching in the framework of Bayes lemma. First, we employ the quantitative occlusion for the least soft-threshold squares based tracker to measure the extent of interference of outlier of observed samples. Then, we sieve the observed object again with the compressed Haar-like feature matching when the single-frame matching response of the tracker is very low. Meanwhile, by reducing the number of independent observed samples through the observed confidence coefficient, the computation complexity can be reduced. The experimental results show that the proposed method can be more effective than other methods.