Detection evaluation and testing region incoming people's in a simple camera view

Moving object detection is considered as a crucial phase of automatic video surveillance systems, particularly, people detection is the first important step in any technique of video analysis processes which can be divided in many stations as motion estimation, tracking people etc. Several methods have been developed for this problem of separating the foreground and background pixels in video surveillance. This paper focuses on computable evaluation of some people detection algorithms for four different video sequences. Our study is based on quantitative and qualitative results respectively by calculating the loss of foreground pixels. Particularly, Three methods have been evaluated by using two metrics: False Negative Error (FNE) and False Positive Error (FPE). In the result we choose the algorithm witch minimize the Error (%). Practically the good technique which dominates on the video surveillance applications is the statistical representation of pixels in foreground which named Gaussian Mixture Model (GMM). In the second part of this paper we control the people entering in a supervised region and we trigger an alarm system in order to find out person presence.