An improved particle filter for multi-feature tracking application

In order to improve the accuracy and robustness of real-time tracking system, this paper presents new methods for efficient object tracking in video sequences using multiple features and particle filter. Based on the problem that tracking with a single feature is susceptible to interference, the color and edge orientation features are combined under the particle filtering framework, and an adaptive feature-weight assignment approach is also proposed in the process of feature fusion. In the prediction period of particle filter algorithm, the mean-shift method is used to improve the particle swarm optimization algorithm. In this way, the number of effective particles is increased and the real-time performance of the tracking system is improved. Experiment results show that the proposed tracking system is more accurate and more efficient than the traditional color feature based mean-shift algorithm.