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

摘要： 针对智能视频监控系统的要求，设计了一个基于视频监控的自动多人脸跟踪识别系统，该系统的功能是实时跟踪视频监控范围内的人脸并鉴别人脸的身份。针对复杂背景及类似人脸区域的影响，提出了一种Adaboost人脸检测算法和主动形状模型相结合的人脸检测算法，实现人脸的准确检测；针对视频监控范围内人脸偏转、交错以及由于人员不断出入而导致人脸数目发生变化的问题，提出了CamShift和Kalman滤波器相结合的多人脸跟踪算法，同时对跟踪到的人脸进行实时身份识别。实验证明，该系统在视频监控范围内对人脸检测和身份识别准确，跟踪实时性好，是一种建立实时视频监控系统的实用方法。

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