Swimmer localization from a moving camera
At the highest level of competitive sport, nearly all performances of athletes (both training and competitive) are chronicled using video. Video is then often viewed by expert coaches/analysts who then manually label important performance indicators to gauge performance. Stroke-rate and pacing are important performance measures in swimming, and these are previously digitised manually by a human. This is problematic as annotating large volumes of video can be costly, and time-consuming. Further, since it is difficult to accurately estimate the position of the swimmer at each frame, measures such as stroke rate are generally aggregated over an entire swimming lap. Vision-based techniques which can automatically, objectively and reliably track the swimmer and their location can potentially solve these issues and allow for large-scale analysis of a swimmer across many videos. However, the aquatic environment is challenging due to fluctuations in scene from splashes, reflections and because swimmers are frequently submerged at different points in a race. In this paper, we temporally segment races into distinct and sequential states, and propose a multimodal approach which employs individual detectors tuned to each race state. Our approach allows the swimmer to be located and tracked smoothly in each frame despite a diverse range of constraints. We test our approach on a video dataset compiled at the 2012 Australian Short Course Swimming Championships.
© Copyright 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA). Published by IEEE. All rights reserved.
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| Notations: | technical and natural sciences endurance sports |
| Published in: | International Conference on Digital Image Computing: Techniques and Applications (DICTA) |
| Language: | English |
| Published: |
Hobart
IEEE
2013
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| Online Access: | http://doi.org/10.1109/DICTA.2013.6691533 |
| Pages: | 1-8 |
| Document types: | congress proceedings |
| Level: | advanced |