Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.667490
Title: Automated tracking of swimmers in the clean swimming phase of a race
Author: Hudson, Christopher Robert
ISNI:       0000 0004 5361 0865
Awarding Body: Sheffield Hallam University
Current Institution: Sheffield Hallam University
Date of Award: 2015
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Abstract:
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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.667490  DOI: Not available
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