Towards AI enabled automated tracking of multiple boxers
This work addresses the challenge of automated athlete tracking for boxing training, providing incremental improvements in segmentation and re-identification.
The paper tackles the problem of continuous tracking of multiple boxers across training sessions using a single fixed top-view camera, achieving 90% accuracy in bout transition detection and zero ID updates or switches in player identification on a custom dataset of 11 hours.
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.