Automated tracking of swimmers in the clean swimming phase of a race

HUDSON, Christopher (2015). Automated tracking of swimmers in the clean swimming phase of a race. Doctoral, Sheffield Hallam University.

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    The current advice for a sports analyst when filming a large performance area is to use multiple fixed cameras or a single panning one. Neither of these options is ideal: multiple cameras must be positioned, have their shutters synchronised and their footage combined for analysis; a panning camera makes it difficult to determine an athlete’s movement relative to an external frame of reference. The aim of this study was to establish a process that enabled the confident, accurate and precise use of a wide field of view for measuring distance and speed in large performance areas. Swimming was used as an example sport as it had a large performance area, which measured 50 m by 25 m. A process for determining the accuracy and precision with which distance and speed could be reconstructed from a wide field of view was developed. A nonlinear calibration procedure was used to account for radial distortion. The Root Mean Square Error (RMSE) of reconstructed distances for a wide field of view was 16 x 10-3 m. This compared favourably with a three camera system reported in the literature, which had an RMSE of 46 x 10-3 m. In addition, it was shown that a wide field of view could be used to identify a 1% enhancement in speed when it was measured over 10 m or more. A wide field of view was used to capture video footage of a swimming competition. This was used to track swimmers using two methods: manual and automated. The two methods showed good agreement for mean speed, but the automated one had higher variability in instantaneous speed than did the manual.

    Item Type: Thesis (Doctoral)
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Centre for Sports Engineering Research
    Depositing User: Helen Garner
    Date Deposited: 24 Sep 2015 11:09
    Last Modified: 26 Apr 2021 12:58

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