A Robust and Real-time Algorithm for Human Face Tracking Using Improved Particle Filtering

摘要：

In view of the problem that face tracker based on particle filtering using only histogram cue is frequently disturbed by background, a particle swarm optimization particle filtering(PSOPF) face tracking algorithm is proposed. An AdaBoost classifier is used to initialize the target tracking and update the template. To solve the problem of degeneration, the distribution of particles is optimized by PSO. Experimental results show that the proposed algorithm can track the human face steadily and be robust to the rotation of face, illumination changes, background interference and partial occlusion. The demand for general real-time performance(30 fps) can also be satisfied.

关键词： Human Face Tracking Particle Filtering Particle Swarm Optimization

作者: Qichang Duan Qi Zhou Pan Duan

作者单位: College of Automation, Chongqing University, Chongqing 400044 College of Electrical Engineering, Chongqing University, Chongqing 400044

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 2421-2425

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）