关键词: 视频监控, Adaboost, 主动形状模型, CamShift, Kalman

Abstract: For requirements of intelligent video surveillance system, this paper presents an automatic multiple face tracking recognition system based on video surveillance, which can track multiple faces real-timely and recognize the identity. Aiming at the influence of complex background and similar to the face region, it puts forward a face detection algorithm based on both Adaboost face detection algorithm and Active Shape Model, and realizes the face detection accurately; a multiple faces tracking algorithm combining CamShift with Kalman filter is proposed for many faces deflection, interlaced and the number changes in the video surveillance scene. Meanwhile, the algorithm also can identify the faces which have been tracked. The experiment results show that in the video surveillance, the system is capable of improving the accurate rate of faces detection and recognition, and it also can track the real-time faces effectively. It is a practical method for developing visual surveillance system.

Key words: video surveillance, Adaboost, Active Shape Model（ASM）, CamShift, Kalman