Abnormal event detection based on trajectory clustering by 2-depth greedy search

Clustering-based approaches for abnormal video event detection have been proven to be effective in the recent literature. Based on the framework proposed in our previous work [1], we have developed in this paper a new strategy for unsupervised trajectory clustering. More specifically, an informationbased trajectory dissimilarity measure is proposed, based on the Bayesian information criterion (BIC). In order to minimize BIC, the agglomerative hierarchical clustering is applied using a 2-depth greedy search process. This strategy achieves better clustering results compared to the traditional 1-depth greedy search. The increased computational complexity is addressed with several bounds on the trajectory dissimilarity.

• Type: Conference
• Year: 2008
• Where: ICASSP
• Authors: Fan Jiang, Ying Wu, Aggelos K. Katsaggelos
• Keywords: Greedy Search, ICASSP 2008, Signal Processing, Trajectory Dissimilarity, Unsupervised Trajectory Clustering