期刊名称：International Journal of Signal Processing, Image Processing and Pattern Recognition
印刷版ISSN：2005-4254
出版年度：2016
卷号：9
期号：10
页码：195-204
出版社：SERSC
摘要：Recently, the compressive tracking (CT) method has attracted much attention due to its high efficiency. However, the CT extracts samples around the previous target region within a fixed search radius; thesearching area is unsuitable when the target undergoes abrupt acceleration change. Meanwhile, the classifier learns the features of the target online without judgmenteven the target is fully occluded. Thus, the improper searching area and incorrectly updated features lead to a marked drop in precision of tracking.To solvethis issue, a robust target tracking method integrating spatio-temporal model to constrain thesearching area is proposed in this paper. Different from CT, the proposed method initially constructs the spatio-temporal model to calculate a confidence map between consecutive frames, and the region with high confidence suggests the high possibility that target exists. Thus the samples can be extracted in the high confidence area. Then, the optimal target location can be estimated with a naive Bayes classifier using sparse coding features.Experiments show that the proposed method outperforms several competing methods in efficiency and robustness.
