Detecting Critical Situation in Public Transport

This paper presents a system information applied to video surveillance to detect and identify aggressive behaviors of people in public transport. A competitive neural network is proposed to form a background model for detecting objects in motion in the sequence. After identifying the objects and extracting its features a set of rules are applied to decide if an anomalous behavior is or not considered aggressive. Our approach is integrated in a CCTV system and is a powerful support tool for security operators to manage to detect in real time critical situations in public transport.