Relevant Instance Segmentation in American Football Practice Images to Aid Risky Tackle Detection

Nasik Muhammad Nafi, Ashley Rediger, Scott Dietrich, William H. Hsu

Abstract: This paper addresses the problem of relevant region segmentation, as a pretext task for a defined multi-object scene classification task, with a specialized application to risky tackle detection from American football practice videos. The downstream task of classifying each frame from such a video as depicting a risky tackle or not depends on the interaction between the tackle-performing player and the target dummy. In both automated and manual approaches, if these two objects can not be differentiated from other objects as part of the analysis, false positive and false negative scene misclassification errors may result, to the detriment of both precision and recall. While player detection appears to be a simple human detection task, specific poses and occlusion due to the dummy make the instance segmentation task particularly challenging in the case of American football practice videos. In this paper, we present a new annotated dataset of tackle practice images and for the first time demonstrate instance segmentation in American football practice images leveraging the new dataset. Further, we show that the Cascade Mask R-CNN based segmentation approach is more suitable for the problem than another popular segmentation model, simple Mask R-CNNs, by characterizing the inherent difficulty of the task and comparing experimental results.

External IDs:dblp:conf/icmla/NafiRDH23