Clustering Trajectories in Heterogeneous Representations for Video Event Detection

Trajectories have been shown to be robust and widely used in surveillance video event analysis. They encode spatial and temporal evidence simultaneously. Hence, clustering trajectories in a video can detect representative events. How to effectively represent trajectories is thus essential to video event detection. However, no a single representation of trajectories suffices in increasingly complex video analysis tasks. To address this issue, this paper presents a hierarchical clustering algorithm for grouping trajectories in multiple heterogeneous representations. It turns out that our method can not only group trajectories of highly similar events but also identify rare events from the dominant events. Experimental results show that our method can retrieve both dominant events and rare events compared with the state-of-the-art methods, leading to a better performance.