Modifed Re-sampling Based Particle Filter for Visual Tracking with MPH

In this paper we propose a particle filter based strategic approach to enhance the performance of visual tracking system with a new re-sampling algorithm. In any particle filter based application especially in visual tracking system, re-sampling is a vital process in the implementation of particle filtering. Usually it is a linear function of particle weight calculation to know the number of particle copies. But, in visual tracking problem it makes the system computationally expensive to make real time performance. We can take more effective particle which has high weight by changing re-sampling as non-linear function. For smooth tracking Maximum a Posteriori (MAP) is introduced for better observation likelihood calculations. Experiments of proposed tracker show that our system is robust against false target tracking, severe occlusion, and rotation, scaling without extra computational cost. We evaluate our system with MPH measurement techniques, and demonstrate that, our proposed new re-sampling algorithm works well.