Identifying anomalous motion behavior in video sequences is a challenging task. Manual annotation of a large number of surveillance
videos is time-consuming because of the limited human brain"es visual attention. This work presents a new framework to detect
abnormalities from unlabeled videos using motion patterns for the normal and abnormal event. This paper proposed an unsupervised
hierarchical agglomerative clustering technique for finding the abnormal behavior motion patterns. Dense trajectories of feature points
were extracted and grouped into feature points for different interval groups with characteristics of the feature points"e motion speed.
With results from partitioning interval groups by hierarchical clustering, anomalous motion patterns were localized in surveillance
video sequences. We performed experiments on publicly available datasets containing different abnormal samples. The experimental
results showed that the proposed framework achieved the highest frame-level accuracy of 96.68% for the UMN dataset. The experiment
has achieved the highest rate of detection (up to 98.63%) for UCSD pedestrian datasets. The proposed framework has achieved
outstanding performance in both pixel level and frame level evaluation.
Keywords
Anomaly detection, Dense trajectories, Hierarchical clustering, Motion pattern, Surveillance video