Face tracking with an Adaptive Adaboost-based Particle Filter
Jianfang Dou, Jianxun Li, Zhi Zhang, Shan Han · 2012
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.