Towards AI enabled automated tracking of multiple boxers

• URL: http://arxiv.org/abs/2311.11471v1
• Date: Wed, 9 Aug 2023 16:46:21 GMT
• Title: Towards AI enabled automated tracking of multiple boxers
• Authors: A.S. Karthikeyan, Vipul Baghel, Anish Monsley Kirupakaran, John Warburton, Ranganathan Srinivasan, Babji Srinivasan, Ravi Sadananda Hegde
• Abstract summary: Continuous tracking of boxers across multiple training sessions helps quantify traits required for the well-known ten-point-must system. This work summarizes our progress in creating a system in an economically single fixed top-view camera. Specifically, we describe improved algorithm for bout transition detection and in-bout continuous player identification without erroneous ID updation or ID switching.
• Score: 0.7067443325368975
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Continuous tracking of boxers across multiple training sessions helps quantify traits required for the well-known ten-point-must system. However, continuous tracking of multiple athletes across multiple training sessions remains a challenge, because it is difficult to precisely segment bout boundaries in a recorded video stream. Furthermore, re-identification of the same athlete over different period or even within the same bout remains a challenge. Difficulties are further compounded when a single fixed view video is captured in top-view. This work summarizes our progress in creating a system in an economically single fixed top-view camera. Specifically, we describe improved algorithm for bout transition detection and in-bout continuous player identification without erroneous ID updation or ID switching. From our custom collected data of ~11 hours (athlete count: 45, bouts: 189), our transition detection algorithm achieves 90% accuracy and continuous ID tracking achieves IDU=0, IDS=0.

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