  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.
