Visual tracking is a core technology for the application domains of intelligent monitoring

robotics and vision navigation

etc.Aiming at the problem of high complexity and poor real-time performance in the existing quasi-sequential Monte Carlo tracking algorithms

this paper presents a method based on multilevel dynamic layer representations

which simulates the posteriori probability of a state using more reliable and effective particles.Then a sampling strategy is proposed in confidence areas derived from the detector

in which each particle represents a dynamic representation and has a two-layer motion model.The observation model based on parted-mean-shift is constructed for space and appearance information.Depending on the degree of matching sub-blocks

the weight of particles is calculated and a way to detect the occlusion state of an object is put forward for realtime model update.Experimental results using challenging public video sequences show better accuracy and efficiency of the proposed method

compared with the classical particle filter and mean-shift algorithms

etc.