Abstract-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.
In REF a Dynamic Oriented Graph (DOG) is used to structure common patterns of objects activities.
Prediction of Abnormal Behaviors for Intelligent Video Surveillance Systems
2007 IEEE Symposium on Computational Intelligence and Data Mining
2007 IEEE Symposium on Computational Intelligence and Data Mining
Computer Science