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Continuous tracking of boxers across multiple training sessions helpsquantify traits required for the well-known ten-point-must system. However,continuous tracking of multiple athletes across multiple training sessionsremains a challenge, because it is difficult to precisely segment boutboundaries in a recorded video stream. Furthermore, re-identification of thesame athlete over different period or even within the same bout remains achallenge. Difficulties are further compounded when a single fixed view videois captured in top-view. This work summarizes our progress in creating a systemin an economically single fixed top-view camera. Specifically, we describeimproved algorithm for bout transition detection and in-bout continuous playeridentification without erroneous ID updation or ID switching. From our customcollected data of ~11 hours (athlete count: 45, bouts: 189), our transitiondetection algorithm achieves 90% accuracy and continuous ID tracking achievesIDU=0, IDS=0.

Towards AI enabled automated tracking of multiple boxers