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Deep Swim Pose

From Multimedia Computing Lab - University of Augsburg

Sample detection and extracted stroke rate.


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 project is to build a human pose detector based on recent advancements in the field of deep learning. Accurately estimated joint position are used for a precise and reliable derivation of different kinematic parameters.


For more information please contact Dan Zecha

References:

  • Dan Zecha, Christian Eggert, Rainer Lienhart, Pose Estimation for Deriving Kinematic Parameters of Competitive Swimmers, Computer Vision Applications in Sports, part of IS&T Electronic Imaging 2017, Burlingame, California, January 2017. [PDF] (to appear)
  • 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]