Trajectory-based anomalous behaviour detection for intelligent traffic surveillance

This study proposes an efficient anomalous behaviour detection framework using trajectory analysis. Such framework includes the trajectory pattern learning module and the online abnormal detection module. In the pattern learning module, a coarse-to-fine clustering strategy is utilised. Vehicle trajectories are coarsely grouped into coherent clusters according to the main flow direction (MFD) vectors followed by a three-stage filtering algorithm. Then a robust K-means clustering algorithm is used in each coarse cluster to get fine classification by which the outliers are distinguished. Finally, the hidden Markov model (HMM) is used to establish the path pattern within each cluster. In the online detection module, the new vehicle trajectory is compared against all the MFD distributions and the HMMs so that the coherence with common motion patterns can be evaluated. Besides that, a real-time abnormal detection method is proposed. The abnormal behaviour can be detected when happening. Experimental results illustrate that the detection rate of the proposed algorithm is close to the state-of-the-art abnormal event detection systems. In addition, the proposed system provides the lowest false detection rate among selected methods. It is suitable for intelligent surveillance applications.