Clustering Based on Two Layers for Abnormal Event Detection in Video Surveillance

Abstract

Abnormal event detection has attracted great research attention in video surveillance. In this paper, the authors presented a robust method of trajectories clustering for abnormal event detection. This method is based on two layers and benefits from two well-known clustering algorithms: the agglomerative hierarchical clustering and the k-means clustering. Facing to the challenges related to the trajectories, e.g., different sizes, the authors introduce a preprocessing step to unify their sizes and reduce their dimensionality. The experimental results show the performance and accuracy of their proposed method.

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“…The detection of both normal (e.g., [1][2][3][4][5][6][7][8][9][10][11][12]) and abnormal (e.g., [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]) video events is a cardinal chore of a surveillance camerasystem. An automated camera system can provide goodtrajectories of objects.…”