Object tracking by applying mean-shift algorithm into particle filtering

Abstract

In the pursuit of robust object tracking, both particle filter and mean-shift algorithm have proven successful approaches. Also both of them have weaknesses. The article presents the integration of mean-shift algorithm with particle filtering during the moving object tracking. In our method mean-shift algorithm is used in the sampling steps of particle filtering, which efficiently reduces the number of sampled particles. That integrates the advantages of mean-shift algorithm and particle filtering. When applied in the moving object tracking, our method proved to be more robust and time saving compared with the conventional particle filtering and mean shift algorithm.