¹College of Computer and Communication, Lanzhou University of Technology, Lanzhou ,China
²College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China
³Key Laboratory of Gansu Advanced Control for Industrial Processes ,Lanzhou, China
⁴Manufacturing Engineering Technology Research Center of Gansu, Lanzhou, China
⁵PLA troops of 91666, Zhoushan Zhejiang 316000, China

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Abstract—A new kind of particle filter is proposed for the state estimation of nonlinear system. The proposed algorithm based on Quadrature Kalman Filter by using integral pruning factor, which optimizes and reorganizes the integration point. New algorithm overcomes the particle degeneration phenomenon well by using Pruning Quadrature Kalman Filter to produce optimized proposal distribution function. In the improving particle filter framework, using color and motion edge character as observation model. Fusing feature weights through the D-S evidence theory, and effectively avoid the questions of bad robust produced by the single color feature in the illumination of mutation, posture change and similar feature occlusion. Experiment results indicate that the proposed method is more robust to track object and has good performance in complex scene.

Index Terms—Particle Filter, Quadrature Kalman Filter, Object Tracking, Multi-feature Fusion, D-S Evidence Theory.

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Cite: Jie Cao, Wei Li, Di Wu, "Multi-feature Fusion Tracking Based on A New Particle Filter," Journal of Computers vol. 7, no. 12, pp. 2939-2947, 2012.