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
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.