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 (F. Jiang et al.,2007), we have developed in this paper a new strategy for unsupervised trajectory clustering. More specifically, an information- based 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.