Abstract: The major aim for modern video surveillance is to capture clear imagery of Region of Interest (ROI) in large-scale and scene. However, it is hard to achieve the goal by zooming when the ROI is far from the surveillance system. A station-motion camera system was designed to acquire the detailed and overall information of pedestrian at distance to obtain the facial feature. A stationary wide Field Of View (FOV) camera was used to monitor an environment for detecting pedestrians by improved Codebook background subtraction. Then a Pan-Tilt-Zoom (PTZ) camera was steered to track a target detected in the stationary camera, and zoomed in it speedily when pedestrian stopped moving. Afterwards, a face detection procedure used the images received from PTZ camera to obtain pedestrian face. Once the face was detected, PTZ camera put it in the center of the FOV and zoomed to acquire high-definition image. While the pedestrian continued moving, PTZ camera received the location of pedestrian from the station camera and tracked it continually. The experiments on real indoor and outdoor environments show that the proposed wide FOV camera pedestrian detection algorithm is robust to illumination variations, and the PTZ camera can track and zoom in the face of pedestrians. Besides, the speed of the method meets the real-time requirement.

Key words: Codebook, dual-camera cooperation, face detection, tracking

摘要： 现代视频监控系统需要获取大范围场景中感兴趣目标的清晰图像，这在目标距离较远并且不断移动时单纯采用摄像机调焦方式通常有一定的困难。为了获取宽范围监控场景中远距离行人的主要面部特征，采用广角静止—窄视场运动双摄像机协同工作方式可以同时获得远距离目标的全局和细节信息。首先采用改进的Codebook背景减法从广角摄像机中检测运动目标，然后指引运动摄像机近距离跟踪观察；若行人停止运动，则利用运动摄像机对其进行放大，然后从中检测人脸，并将人脸置于视野中心放大得到清晰图像。当行人再次运动时，广角相机将初始位置再次传递给运动摄像机，由其再对行人进行跟踪。通过实验室内和室外真实场景的实验表明，广角相机的检测算法具有一定的鲁棒性，运动相机能跟踪放大行人人脸图像，算法运行速度满足实时性要求。

关键词: 码书, 双摄机协同, 人脸检测, 跟踪