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.
@article{arxiv.2311.11471,
title = {Towards AI enabled automated tracking of multiple boxers},
author = {A. S. Karthikeyan and Vipul Baghel and Anish Monsley Kirupakaran and John Warburton and Ranganathan Srinivasan and Babji Srinivasan and Ravi Sadananda Hegde},
journal= {arXiv preprint arXiv:2311.11471},
year = {2023}
}