People counting system

Demand for security and surveillance systems is getting bigger day after day. This work proposes a method that counts people and detects suspicious attitudes via video sequences of areas with moderate people access. A typical application is the security of warehouses during the night, on weekends or at any time when people access is allowed but no load movement is admissible. Specifically it focuses on detecting when a person passing by the environment carries any object belonging to the background away or leaves any object in the background, while only people movement is allowed in the area. In addition, it estimates the number of people on scene. The method consists of performing four main tasks on video sequences: a) background and foreground separation, b) background estimative dynamic update, c) people location and counting, and d) suspicious attitudes detection. The proposed background and foreground separation and background estimative update algorithms deal with illumination fluctuation and shade effects. People location and counting explores colour information and motion coherence. A prototype implementing the proposed method was built for evaluation purpose. Experiments on simulated and real video sequences are reported showing the effectiveness of the proposed approach.