  Tracking players in sports videos is commonly done in a tracking-by-detection
framework, first detecting players in each frame, and then performing
association over time. While for some sports tracking players is sufficient for
game analysis, sports like hockey, tennis and polo may require additional
detections, that include the object the player is holding (e.g. racket, stick).
The baseline solution for this problem involves detecting these objects as
separate classes, and matching them to player detections based on the
intersection over union (IoU). This approach, however, leads to poor matching
performance in crowded situations, as it does not model the relationship
between players and objects. In this paper, we propose a simple yet efficient
way to detect and match players and related objects at once without extra cost,
by considering an implicit association for prediction of multiple objects
through the same proposal box. We evaluate the method on a dataset of broadcast
ice hockey videos, and also a new public dataset we introduce called COCO
+Torso. On the ice hockey dataset, the proposed method boosts matching
performance from 57.1% to 81.4%, while also improving the meanAP of
player+stick detections from 68.4% to 88.3%. On the COCO +Torso dataset, we see
matching improving from 47.9% to 65.2%. The COCO +Torso dataset, code and
pre-trained models will be released at
https://github.com/foreverYoungGitHub/detect-and-match-related-objects.
