In team sports scenes, such as in handball, it is common to have many players on the field performing different actions according to the rules of the game. During practice, each player has their own ball, and sequentially repeats a particular technique in order to adopt it and use it. In this paper, the focus is to detect and track all players on the handball court, so that the performance of a particular athlete, and the adoption of a particular technique can be analyzed. This is a very demanding task of multiple object tracking because players move fast, often change direction, and are often occluded or out of the camera field view. We propose a DeepSort algorithm for player tracking after the players have been detected with YOLOv3 object detector. The effectiveness of the proposed methods is evaluated on a custom set of handball scenes using standard multiple object tracking metrics. Also, common detection problems that have been observed are discussed.
© Copyright 2020 Published by Croatian Scientific Bibliography. All rights reserved.