Optimized human targets detection in surveillance scenes

This paper presents a new human targets detection algorithm for video surveillance scenes. We propose a new approach for estimation of the existence of human targets, which includes multiple modes. Based on the detection structure, different features of human targets can be extracted. There are mainly two folds of advantage. First, the system integrates the time filtration and proportion of reliability weight, in order to intelligently merge and reinforce the results of different kinds of sensors. Second, it uses a short time video analysis, which can detect the human face by a Hough Transform, in order to confirm human targets obtained by the first step. Furthermore, before face detection, we use the gray similarity for local region clustering from a whole video frame, by which the range of Hough Transform can be reduced. By these processes, the accuracy of human targets detection can be increased effectively, and the video surveillance system can be switched on regions where human bodies are detected. Therefore, it can save system resources, and enhance the whole efficiency.