Learning Patterns of Motion Trajectories Using Real-Time Tracking
Ying Feng Cai, Hai Wang, Wei Gong Zhang · Advanced materials research · 2011
The understanding and description of behaviors for road vehicles is a hot topic of intelligent visual surveillance system. Trajectory analysis is one of the basic problems in behavior understanding, from which anomalies can be detected and also accidents can be predicted. In this paper, we proposed a hierarchical self-organizing neural network model to learn trajectory distribution pattern and a probability model for accident recognition. Sample data including motion trajectories are first get by real-time vehicle tracking. The self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Using the learned patterns, we consider anomaly detection as well as object behavior prediction. Experiments in actual road scene show the effectiveness of the proposed algorithm.