Abstract Particle filter (PF) fails when the tracked object is occluded by other objects or its appearance changes. In this paper, human memory model is introduced into the template updating process of particle filter, which is inspired by the human memory mechanism, and a memory-based particle filter (MPF) algorithm is proposed. Each template is processed and transferred through ultra-short time memory space, short time memory space and long time memory space. The proposed memory-based model can remember what the template used to be, which helps the model adapt to the variation of object’s appearance more quickly. The experimental results show the effectiveness of the proposed method.
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2012, Vol. 25 
