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

Boyle, J. ORCID: https://orcid.org/0000-0002-5785-8046, Nawaz, T. and Ferryman, J. ORCID: https://orcid.org/0000-0003-2194-4871 (2022) Using Deep Siamese networks for trajectory analysis to extract motion patterns in videos. Electronics Letters. ISSN 0013-5194 doi: 10.1049/ell2.12460

Abstract/Summary

Abstract: 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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/104220
Item Type	Article
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Uncontrolled Keywords	Image and Vision Processing and Display Technology, Letter
Publisher	Institution of Engineering and Technology (IET)
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