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Paper Detail

Paper IDIFS-2.8
Paper Title ACTION SEGMENTATION ON REPRESENTATIONS OF SKELETON SEQUENCES USING TRANSFORMER NETWORKS
Authors Simon Häring, Raphael Memmesheimer, Dietrich Paulus, University of Koblenz-Landau, Germany
SessionIFS-2: Information Forensics and Security
LocationArea K
Session Time:Monday, 20 September, 15:30 - 17:00
Presentation Time:Monday, 20 September, 15:30 - 17:00
Presentation Poster
Topic Information Forensics and Security: Surveillance
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract We propose an approach for action segmentation by representing motions as images. A transformer object detection network is used to segment the sequences from the representation images. We examine different encoding approaches, normalization strategies and skeleton joint orders in an extensive experiment study. Our approach is evaluated on skeleton sequences from the PKU-MMD dataset. We successfully apply transformer networks for action segmentation on skeleton sequences. Our proposed approach achieves high class accuracies while start and end-time estimation of the action segments are subject to further improvement.