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Swimmer Detection and Pose Estimation for Continuous Stroke Rate Determination

From Multimedia Computing Lab - University of Augsburg

(Redirected from Swimmer pose estimation)


The success of a professional athlete depends quite strongly on the assessment and active improvement of his or her technique. In the field of competitive swimming, a quantitative evaluation is highly desirable to supplement the typical qualitative analysis. However, quantitative (manual) evaluations are very time consuming and therefore only used in individual cases.

In a joint project with the Institute of Applied Training Science in Leipzig (Institut für angewandte Trainingswissenschaften, IAT), we are developing a system for detecting a swimmer in a swimming channel and continuously estimating his or her pose in order to capture (inner-)cyclic structures and derive kinematic parameters for a biomechanical analysis. Human pose recovery in aquatic environments faces a lot of challenges, from heavily cluttered fore- and background to partial occlusion.

The purpose of this work is two-fold: firstly, we are developing a robust method for accurately detecting individual key poses with specifically trained object detectors. The procedure is fully automatic and retrieves stroke frequency, stroke length and inner-cycle intervals. Secondly, we optimize our approach in terms of time consumption through algorithmic optimizations, parallelization and GPU programming, allowing for real time application of our system.

Sample detection and extracted stroke rate.



For more information please contact Dan Zecha

References:

  • Dan Zecha and Rainer Lienhart. Key-Pose Prediction in Cyclic Human Motion. IEEE Winter Conference on Applications of Computer Vision 2015 (WACV15), Waikoloa Beach, HI, January 6-9, 2015 [PDF]
  • Dan Zecha, Thomas Greif, and Rainer Lienhart. Swimmer Detection and Pose Estimation for Continuous Stroke Rate Determination. Multimedia Content Access: Algorithms and Systems VI, part of IS&T/SPIE Electronic Imaging, 23 January 2012, Burlingame, California, USA
    Also Technical Report 2011-13, University of Augsburg, Institute of Computer Science, July 2011. [PDF] [Video]
  • Dan Zecha and Rainer Lienhart. Bestimmung intrazyklischer Phasengeschwindigkeiten von Schwimmern im Schwimmkanal mittels vollautomatischer Videoanalyse. Technical Report 2014-04, University of Augsburg, Institute of Computer Science, July 2014. [PDF]