A Sparse Attention Pipeline for DeepSportRadar Basketball Player Instance Segmentation Challenge

Fang Gao, Wenjie Wu, Yan Jin, Lei Shi, Shengheng Ma

Abstract: The ACM MMSports2023 DeepSportRadar Basketball Player Instance Segmentation Challenge was focused on addressing the issue of occlusion. The dataset's primary characteristics include vast background areas, a high degree of occlusion between athletes, and limited data volume. To tackle the challenge of severe occlusion among athletes, we developed a sparse attention pipeline. Firstly, we introduced the InternImage backbone network and Sparse Multi-Head Self-Attention Module with a sparse Transformer. This allowed the segmentation pipeline to prioritize critical regions, effectively dealing with occlusion and class imbalance issues. Secondly, we adopted a multi-scale processing strategy using the Simple Dual Refinement Feature Pyramid Networks (SDRFPN) to fuse features of different scales. This approach improved the ability to handle athletes' features with different scales and fine details. Lastly, during the training phase, we employed random flipping data augmentation, which assisted the segmentation pipeline in recognizing targets from various angles and orientations. On the DeepSportRadar Basketball Player Instance Segmentation Challenge dataset, the pipeline achieved an impressive Occlusion Metric (OM) score of 0.316.