Unusual event detection in crowded scenes by trajectory analysis

Anomaly detection in crowded scenes is a challenge task due to variation of the definitions for both abnormality and normality, the low resolution on the target, ambiguity of appearance, and severe occlusions of inter-object. In this paper, we propose a novel statistical framework to detect abnormal behaviors of the crowded scene by modeling trajectories of pedestrians. First, the trajectories are acquired by Kanade-Lucas-Tomasi Feature Tracker (KLT). Then trajectories are grouped to form representative trajectories, which characterize the underlying motion patterns of the crowd. Finally, trajectories are modeled by Multi-Observation Hidden Markov Model (MOHMM) to determine whether frames are normal or abnormal. The experiments are conducted on a well-known crowded scene dataset. Experimental results show that the proposed method can capture abnormal crowd behaviors successfully and achieves state-of-the-art performances.