Multi-level trajectory learning for traffic behavior detection and analysis

Motion patterns can be learnt automatically based on object trajectories data extracted by means of video tracking, which is an effective approach for modeling and analyzing traffic behavior. In this paper, a multi-level motion pattern learning approach for traffic behavior analysis is presented, which takes into account the spatial characteristics, direction characteristics, and type characteristics of trajectories. At the spatial level, improved Hausdorff distance measurement is applied to construct a spatial similarity matrix of the trajectories collected, and spectral clustering is used to achieve spatial pattern learning. At the directional level, the start and end points of trajectories are fitted using a Gaussian mixed model to extract the distribution of entry and exit zones. Then, the direction pattern is obtained from the regional centers of the pairwise distribution zones. At the type level, the type pattern is acquired through a K-means clustering algorithm that considers multiple classification features of trajectories. Based on the learned multi-level motion patterns, abnormal behavior detection algorithms are further developed by means of pattern matching. Finally, our approach is tested with several video sequences from real-world traffic scenarios. Some typical traffic behaviors in the test scenarios are successfully recognized and analyzed and examples of abnormal traffic behaviors are also reliably detected.