基于粒子滤波和均值平移的目标跟踪

提出一种粒子滤波和均值平移相结合的跟踪算法,其中均值平移起主导作用,当其失效时会产生少量的粒子进一步搜索,确定目标位置以减少误差.与传统的粒子滤波相比,这种方法只需少量的粒子覆盖可能的目标分布,大大减少了计算量.

Particle Filter and Mean Shift-based Object Tracking

Particle filter and mean shift are two successful approaches taken in the pursuit of robust tracking.Both of them have their respective strengths and weaknesses.In this paper,we propose an approach in which Kernel mean shift is the dominant tracking method,with a small number of particles being generated to explore further and so resist errors when confidence in the mean shift algorithm is low.Compare with the conventional particle filter,our approach require fewer particles to maintain multiple hypotheses,resulting in low computational cost.