关键词: 目标跟踪, AdaBoost分类器, 稀疏表示, 重建误差

Abstract: In order to solve the problem of motion blur which disturbs target gradient information and causes serious occlusion of targets in video sequences, a novel visual tracking algorithm, which combines the MTRSR model with AdaBoost classifier is proposed. The algorithm uses a descriptive dictionary to estimate the weight of each candidates. Firstly, a MTRSR model is utilized to get the blur kernel [k] to get blur target templates set, and meanwhile the confidence of the candidates is also calculated by the reconstruction error. AdaBoost classifier is trained to evaluate the confidence of all candidates. To this end, the HOG features of the target templates are used to encode a descriptive dictionary to calculate the weights of the candidates. Finally, the best target is retrieved by the sum of production of the weight and the two confidences. The experimental results show that the algorithm can efficiently cope with the gradient change caused by motion blur and severe target occlusion in complicated scenes, and further improve the accuracy and robustness of visual tracking.

Key words: visual tracking, AdaBoost classifier, sparse representation, reconstruction error