Prediction of Abnormal Behaviors for Intelligent Video Surveillance Systems

–The OBSERVER is a video surveillance system that detects and predicts abnormal behaviors aiming at the intelligent surveillance concept. The system acquires color images from a stationary video camera and applies state of the art algorithms to segment, track and classify moving objects. In this paper we present the behavior analysis module of the system. A novel method, called Dynamic Oriented Graph (DOG) is used to detect and predict abnormal behaviors, using real-time unsupervised learning. The DOG method characterizes observed actions by means of a structure of unidirectional connected nodes, each one defining a region in the hyperspace of attributes measured from the observed moving objects and having assigned a probability to generate an abnormal behavior. An experimental evaluation with synthetic data was held, where the DOG method outperforms the previously used N-ary Trees classifier.

• Type: Conference
• Year: 2007
• Where: CIDM
• Authors: Duarte Duque, Henrique Santos, Paulo Cortez
• Keywords: Abnormal Behavior, Artificial Intelligence, CIDM 2007, DOG Method, Intelligent Surveillance Concept