Performance Evaluation of Mobile Person Detection and Area Entry Tests through a One-View Camera
Abderrahmane Ezzahout, Youssef Hadi, Rachid Oulad Haj Thami
Journal of Information Organization, Vol. 2, No. 3, 2012.
DOI: 
Link: http://www.dline.info/jio/fulltext/v2n3/4.pdf
Abstract:
In recent years, detection and tracking people from a video stream have been widely studied in many commercial and public surveillance systems. 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 tracking processes which can be divided into many stations such 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. In this study, 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 which minimizes the Error (%). Practically, the good technique which dominates on the video surveillance applications is the statistical representation of pixels in foreground which is named Gaussian Mixture Model (GMM). In the second part of this paper we control the people entering in a supervised region by detection of the first correct pixel incoming to our supervised area, and we trigger an alarm system.