Robust video mining based on local similarity alignment of motion trajectories

Motion trajectory is one of the most important cues for extracting semantic information from video data. Numerous studies have focused on the analysis and comparison of the similarity among motion trajectories. Most of the previous methods rely on a global measure that does not account for partial or lost information. In particular, existing techniques fail to capture the salient features of local events shared by distinct motion trajectories. In this paper, we propose a novel local similarity alignment based method for retrieving similar motion events. Our approach is motivated by the well-known global sequence alignment and BLAST algorithms used in character string matching and genomic analysis. The proposed local similarity alignment measure focuses on key segments of motion trajectories and thus yields superior computational speed while providing improved performance, especially in the presence of missing information. We conduct extensive computer simulations that demonstrate the superiority of the proposed approach in terms of its efficiency and computational speed. Moreover, we highlight the robustness of the proposed local similarity alignment method to information loss (e.g. due to occlusion) by demonstrating its performance for partial motion trajectories.