Improved Object Tracking with Particle Filter and Mean Shift

摘要：

In this paper,we present a new object tracking algorithm based on particle filtering technique and the Mean Shift algorithm.The particle filtering technique is a powerful technique for tracking objects in image sequences with complex background.It has been proved to be a robust method of tracking in non-linear and non-Gaussian case.But two common problems of the particle filter technique are the degeneracy phenomenon and the huge computational cost.To solve these problems,our new tracking algorithm uses the Mean Shift algorithm inside the particle filter.With the help of the Mean Shift algorithm,we can sample more particles of higher weights, and discard those particles whose contribution to the tracking is almost zero.The experiment results show that the new algorithm reduces the degeneracy problem and the computational cost of the particle filter.

关键词： Particle filtering Mean Shift Object Tracking Color distribution

作者: Kejia Bai Weiming Liu

作者单位: College of Automation Science and Engineering　South China University of Technology Guangzhou,Guangdo College of Traffic and Communications South China University of Technology Guangzhou,Guangdong Provi

会议类型: 国际会议

会议名称: 2007 IEEE International Conference on Automation and Lofistics

会议地点: 山东济南

会议语种:英文

在线出版日期: 2007-08-18（万方平台首次上网日期，不代表论文的发表时间）