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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's 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' 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.
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