Abstract
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.
Highlights
Visual object tracking is one of the most fundamental problems in computer vision with numerous applications such as intelligent surveillance, robot environment perception, and augmented reality
We propose a cognitive psychological memory model–based tracking (CPMT) algorithm to address the stability–plasticity dilemma mentioned above
In long-term memory tracker (LMT), thresholds of the 2-D appearance nearest neighbor classifier and 3-D shape nearest neighbor classifier are set to a 1⁄4 0:5 and s 1⁄4 0:5, respectively, and the forgotten threshold of long-term memory is set to f 1⁄4 0:2
Summary
Visual object tracking is one of the most fundamental problems in computer vision with numerous applications such as intelligent surveillance, robot environment perception, and augmented reality. In the visual tracking task, an unknown target object specified in the first frame should be tracked in the subsequent frames. Despite significant progresses in the last decades,[1,2,3] it is still challenging due to illumination variation, deformation, abrupt motion, and occlusion. Visual tracking is relatively easy for humans. The key component of a visual tracker is the online object modeling; correspondingly, humans perceive the environment with three-dimensional (3-D) stereo vision and remember (model) the 3-D object effectively by using the biological memory. We consider to exploit the biological memory of humans to overcome the visual tracking challenges mentioned above
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