Real-Time Swimmer Tracking on Sparse Camera Array
: Paavo Nevalainen, M. Hashem Haghbayan, Antti Kauhanen, Jonne Pohjankukka, Mikko-Jussi Laakso, Jukka Heikkonen
: Ana Fred, Maria De Marsico, Gabriella Sanniti di Baja
: International Conference on Pattern Recognition Applications and Methods
Publisher: Springer
: Chamberra
: 2017
: Pattern Recognition Applications and Methods: 5th International Conference, ICPRAM 2016, Rome, Italy, February 24-26, 2016, Revised Selected Papers
: Lecture Notes in Computer Science
: 10163
: 156
: 174
: 19
: 978-3-319-53374-2
: 978-3-319-53375-9
: 0302-9743
DOI: https://doi.org/10.1007/978-3-319-53375-9_9
: https://link.springer.com/chapter/10.1007/978-3-319-53375-9_9
A swimmer detection and tracking is an essential first step in a video-based athletics performance analysis. A real-time algorithm is presented, with the following capabilities: performing the planar projection of the image, fading the background to protect the intimacy of other swimmers, framing the swimmer at a specific swimming lane, and eliminating the redundant video stream from idle cameras. The generated video stream is a basis for further analysis at the batch-mode. The geometric video transform accommodates a sparse camera array and enables geometric observations of swimmer silhouette. The tracking component allows real-time feedback and combination of different video streams to a single one. Swimming cycle registration algorithm based on markerless tracking is presented. The methodology allows unknown camera positions and can be installed in many types of public swimming pools.