Swimmer detection and pose estimation for continuous stroke rate determination
- In this work we propose a novel approach to automatically detect a swimmer and estimate his/her pose continuously in order to derive an estimate of his/her stroke rate given that we observe the swimmer from the side. We divide a swimming cycle of each stroke into several intervals. Each interval represents a pose of the stroke. We use specifically trained object detectors to detect each pose of a stroke within a video and count the number of occurrences per time unit of the most distinctive poses (so-called key poses) of a stroke to continuously infer the stroke rate. We extensively evaluate the overall performance and the influence of the selected poses for all swimming styles on a data set consisting of a variety of swimmers.
| Author: | Dan ZechaGND, Thomas GreifGND, Rainer LienhartGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-12389 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/1546 |
| Series (Serial Number): | Reports / Technische Berichte der Fakultät für Angewandte Informatik der Universität Augsburg (2011-13) |
| Publisher: | Universität Augsburg |
| Place of publication: | Augsburg |
| Type: | Report |
| Language: | English |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2011/07/18 |
| Tag: | object detection; pose estimation; stroke rate estimation; swimming channel |
| 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 |
