Towards AI enabled automated tracking of multiple boxers

A. S. Karthikeyan; Vipul Baghel; Anish Monsley Kirupakaran; John Warburton; Ranganathan Srinivasan; Babji Srinivasan; Ravi Sadananda Hegde

arXiv · 2311.11471

Towards AI enabled automated tracking of multiple boxers

A. S. Karthikeyan·Vipul Baghel·Anish Monsley Kirupakaran·John Warburton·Ranganathan Srinivasan·Babji Srinivasan·Ravi Sadananda Hegde

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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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A. S. Karthikeyan, Vipul Baghel, Anish Monsley Kirupakaran, John Warburton, Ranganathan Srinivasan, Babji Srinivasan, Ravi Sadananda Hegde. 2023-08-09. Towards AI enabled automated tracking of multiple boxers. https://arxiv.org/abs/2311.11471

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