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Image Based Stroke-Rate Detection System for Swim Race Analysis

1
AMRC Training Centre, University of Sheffield, Rotherham S60 5BL, UK
2
Centre for Sports Engineering Research, Sheffield Hallam University, Sheffield S10 2LW, UK
*
Author to whom correspondence should be addressed.
Presented at the 12th Conference of the International Sports Engineering Association, Brisbane, Queensland, Australia, 26–29 March 2018.
Proceedings 2018, 2(6), 286; https://doi.org/10.3390/proceedings2060286
Published: 23 February 2018
Swim race analysis systems often rely on manual digitization of recorded videos to obtain performance related metrics such as stroke-rate, stroke-length or swim velocity. Using image-processing algorithms, a stroke tagging system has been developed that can be used in competitive swimming environments. Test images from video footage of a women’s 200 m medley race recorded at the 2012 Olympic Games, was segmented into regions of interest (ROI) consisting of individual lanes. Analysis of ROI indicated that the red component of the RGB color map corresponded well with the splash generated by the swimmer. Detected red values from the splash were filtered and a sine-fitting function applied; the frequency of which was used to estimate stroke-rate. Results were compared to manually identified parameters and demonstrated excellent agreement for all four disciplines. Future developments will look to improve the accuracy of the identification of swimmer position allowing swim velocity to be calculated.
Keywords: swimming; image processing; performance analysis swimming; image processing; performance analysis
MDPI and ACS Style

Driscoll, H.; Hudson, C.; Dunn, M.; Kelley, J. Image Based Stroke-Rate Detection System for Swim Race Analysis. Proceedings 2018, 2, 286.

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