基于人类记忆模型的粒子滤波鲁棒目标跟踪算法

当目标被场景中的物体或其它运动目标遮挡，或者目标姿态发生很大改变时，粒子滤波器就会失效。为解决这类问题，受人类记忆机制的启发，文中将人类记忆模型引入到粒子滤波器模板更新过程，提出一种基于记忆的粒子滤波器。每个模板都要经过瞬时记忆、短时记忆和长时记忆3个空间的传输和处理。该粒子滤波器能记住曾经出现的目标模板，从而能更快地适应目标姿态的变化。实验结果验证了该算法的有效性。

Robust Object Tracking Algorithm by Particle Filter Based on Human Memory Model

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.