1-D temporal segments analysis for traffic video surveillance

Traffic video surveillance is an important topic for security purposes and to improve the traffic flow management. Video surveillance can be used for different purposes such as counting of vehicles or to detect their speed and behaviors. In this context, it is often important to be able to analyze the video in real-time. The huge amount of data generated by the increasing number of cameras is an obstacle to reach this goal. A solution consists in selecting in the video only the regions of interest, essentially the vehicles on the road areas. In this paper, we propose to extract significant segments of the regions of interest and to analyze them temporally to count vehicles and to define their behaviors. Experiments on real data show that precise vehicle's counting and high recall and precision are obtain for vehicle's behavior and traffic analysis.