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.

我们通过经济实用的单个固定顶视摄像机，在连续追踪拳击手的过程中，利用改进的算法进行过渡检测和持续的运动员识别，从而在精确分割录制视频流中的拳击回合边界以及不同时间段或同一回合内重新识别同一拳击手的困难中取得了进展，并且在我们自定义采集的约 11 小时数据中，过渡检测算法精度达到 90%，持续追踪的识别错误次数为 0。