基于人工蜂群算法的粒子滤波目标跟踪

针对基于粒子滤波的视频目标跟踪算法中由于粒子重采样过程而导致粒子贫化的问题,提出了一种基于人工蜂群算法的粒子滤波目标跟踪算法,利用群体智能的特点使得粒子集在重采样前得到优化,保持了粒子的多样性,从而解决了粒子贫化问题,同时增加了有效粒子的数目.实验结果表明,基于人工蜂群算法的粒子滤波跟踪算法,比标准粒子滤波跟踪算法所需粒子数更少,对目标遮挡、较复杂背景有较好的跟踪效果.

Artificial Bee Colony algorithm based particle filter for visual tracking

Particle filter algorithm has been proven to be a powerful tool in solving visual tracking problems. However, the problem of sample impoverishment which is brought by the procedure of resampling is a main handicap of the particle filter. In this work, an improved particle filter based on artificial bee colony algorithm is proposed to solve this problem. The particles in the particle filter are optimized based on ABC algorithm before resampling. Thus, the particles can approximate the true state of the target better, and the number of efficient particles can be increased significantly. Experimental results demonstrate that the proposed algorithm can track targets robustly in various challenging conditions which outperforms the standard particle filter.