Video Object Tracing Based on Particle Filter with Ant Colony Optimization

摘要：

Classical particle filter needs large numbers of samples to properly approximate the posterior density of the state evolution. Furthermore, sample impoverishment is an inevitable problem, which is a key issue in the performance of a particle filter. In this paper, a particle filtering algorithm based on ant colony optimization (ACO) was proposed to enhance the performance of particle filter with small sample set. ACO algorithm optimized the sample set before re-sampling step. Target state estimation was computed according to the optimrzed samples. Ant colony algorithm can effectively eliminate particle degeneration and enhance its robustness. Experiment results demonstrate that the proposed algorithm effectively improved the efficiency of video object tracking system.

关键词： ant colony optimization particle filter video tracking

作者: Zhou Hao Xuejie Zhang Pengfei Yu Haiyan Li

作者单位: Information School of Yunnan University Kunming China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 232-236

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）