期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2017
卷号：14
期号：1
DOI：10.1177/1729881417692313
语种：English
出版社：SAGE Publications
摘要：Visual tracking is a challenging computer vision task due to the significant observation changes of the target. By contrast, the tracking task is relatively easy for humans. In this article, we propose a tracker inspired by the cognitive psychological memory mechanism, which decomposes the tracking task into sensory memory register, short-term memory tracker, and long-term memory tracker like humans. The sensory memory register captures information with three-dimensional perception; the short-term memory tracker builds the highly plastic observation model via memory rehearsal; the long-term memory tracker builds the highly stable observation model via memory encoding and retrieval. With the cooperative models, the tracker can easily handle various tracking scenarios. In addition, an appearance-shape learning method is proposed to update the two-dimensional appearance model and three-dimensional shape model appropriately. Extensive experimental results on a large-scale benchmark data set demonstrate that the proposed method outperforms the state-of-the-art two-dimensional and three-dimensional trackers in terms of efficiency, accuracy, and robustness.
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