A Temporal Scores Network for Basketball Foul Classification

Deep learning has developed rapidly in recent years, not only in image recognition, but now also in action recognition. The research on action recognition started with 3D-CNN, which has achieved good results on many tasks. But most action recognition networks have room for improvement in fine-grained action recognition. The reason is that there is only a slight difference between categories in the fine-grained classification task. e.g. basketball fouls only occur in a few frames and a small region. This situation may lead to some errors with 3DCNN methods because these models tend to merge all temporal features. To identify these fouls, it is necessary to strengthen the detection of small periods. In this paper, we propose a temporal score network suitable for existing networks, including 3D-Resnet50, 3D-wide-Resnet50, R(2 +1) D-Resnet50, and I3D-50 to improve the accuracy of fine-grained action recognition. The experimental results show that the accuracy of various models is improved by 3.85% to 6% after adding the proposed network. Since there is no relevant public dataset, we collect the data ourselves to create a basketball foul dataset.