Decoupling video and human motion: towards practical event detection in athlete recordings

  • In this paper we address the problem of motion event detection in athlete recordings from individual sports. In contrast to recent end-to-end approaches, we propose to use 2D human pose sequences as an intermediate representation that decouples human motion from the raw video information. Combined with domain-adapted athlete tracking, we describe two approaches to event detection on pose sequences and evaluate them in complementary domains: swimming and athletics. For swimming, we show how robust decision rules on pose statistics can detect different motion events during swim starts, with a F1 score of over 91% despite limited data. For athletics, we use a convolutional sequence model to infer stride-related events in long and triple jump recordings, leading to highly accurate detections with 96% in F1 score at only +/- 5ms temporal deviation. Our approach is not limited to these domains and shows the flexibility of pose-based motion event detection.

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Author:Moritz EinfaltGND, Rainer LienhartGND
Frontdoor URL
Parent Title (English):IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2020, Seattle, WA, USA, June 2020
Place of publication:Piscataway, NJ
Type:Conference Proceeding
Year of first Publication:2020
Publishing Institution:Universität Augsburg
Release Date:2020/04/21
First Page:3901
Last Page:3910
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Maschinelles Lernen und Maschinelles Sehen
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):Deutsches Urheberrecht