Video motion tracking using enhanced particle filtering with Mean-shift

Some weaknesses of particle filtering has been have been identified from an application perspective. This paper proposes a trial approach to tackle the problems of exceeding number of particles required for sampling, low particle efficiency, and compromised particle diversity resulting from resampling. The method combines particle filtering with Mean-shift, which is used to further optimize the sampled particles, thus significantly reducing the number of particles while retaining the particle diversity. The Bhattacharyya factor is induced to determine the importance weighting of particle. The test results exhibit that the proposed method can perform the robust tracking in the face of high mobility, partial occlusion, and limited rotation.