Face Tracking with an Adaptive Adaboost-Based Particle Filter

摘要：

A novel algorithm, termed a Boosted Adaptive Particle Filter (AAPF), for integrated face detection and face tracking is proposed. The proposed algorithm is based on the synthesis of an adaptive particle filtering algorithm and the AdaBoost face detection algorithm. An Adaptive Particle Filter (AAPF), based on a new sampling technique, is proposed. The APF is shown to yield more accurate estimates of the proposal distribution than the standard Particle Filter thus enabling more accurate tracking in video sequences. In the proposed AAPF algorithm, the AdaBoost algorithm is used to detect faces in input image frames, the APF algorithm incorporate the detection result of AdaBoost algorithm to improve the proposal distribution of the particles. Experimental results show that the proposed AAPF algorithm provides a means for robust face detection and accurate face tracking under various tracking scenarios.

关键词： Particle filter Adaboost Face detection proposal distribution

作者: Jianfang Dou Jianxun Li Zhi Zhang Shan Han

作者单位: Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and In Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and In Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and In

会议类型: 国际会议

会议名称: The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)

会议地点: 太原

会议语种:英文

页码: 3643-4348

在线出版日期: 2012-05-23（万方平台首次上网日期，不代表论文的发表时间）