Using Deep Siamese networks for trajectory analysis to extract motion patterns in videos

This paper investigates the use of Siamese networks for trajectory similarity analysis in surveillance tasks. Specifically, the proposed approach uses an auto-encoder as a part of training a discriminative twin (Siamese) network to perform trajectory similarity analysis, thus presenting an end-to-end framework to perform an online motion pattern extraction in the scene with an ability to incorporate new incoming trajectory(ies) incrementally. The effectiveness of the proposed method is evaluated on four challenging public real-world datasets containing both vehicle and person targets, and compared with five existing methods. The proposed method consistently shows better or comparable performance than the existing methods on all datasets.