Trajectory pattern learning approach based on cascade competitive neural networks and graph search method

A new motion trajectory learning approach was put forward based on cascade competitive neural networks and directed acyclic graph search method. In this approach, the cascade competitive neural networks was trained to discover the distribution of the flow vectors firstly; and then a directed acyclic graph was constructed according to the time relation of the trajectory points; finally, the depth first search method was adopted to obtain the explicit representation of the trajectory pattern. Based on above works, correspondent method was given to detect the abnormal trajectory. The cascade competitive neural networks represent the flow vectors' time orders impliedly and can deal with the problem of trajectory pattern learning with different length properly. The simulation results of different scenes demonstrate that the method is effective for anomaly detection in complicated environments.