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A Target Tracking Algorithm Based on Particle Filter

ZHANG Yang, WANG Xin-min

Abstract


Particle Filter is an efficient tool for non-rigidity objects tracking. In order to improve efficiency in the use of the particles, this paper presents a Bayesian-based PF method for objects tracking in dynamic scenes, and discusses the Bayesian estimation algorithm and the PF process, which used the tools to simulate the whole process. The robust mean technique is chosen to ascertain the position. Through the experiments of single object tracking, the simulation results prove the method’s effectiveness.

Keywords


particle filter, object tracking.

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