A Temporal Scores Network for Basketball Foul Classification

Po Yung Chou, Cheng Hung Lin*, Wen Chung Kao, Yi Fang Lee, Chen Chine Hsu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.

Original languageEnglish
Title of host publication2022 IEEE 12th International Conference on Consumer Electronics, ICCE-Berlin 2022
PublisherIEEE Computer Society
ISBN (Electronic)9781665456760
Publication statusPublished - 2022
Event12th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2022 - Berlin, Germany
Duration: 2022 Sept 22022 Sept 6

Publication series

NameIEEE International Conference on Consumer Electronics - Berlin, ICCE-Berlin
ISSN (Print)2166-6814
ISSN (Electronic)2166-6822


Conference12th IEEE International Conference on Consumer Electronics, ICCE-Berlin 2022


  • deep learning
  • fine-grained action recognition
  • image recognition

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Media Technology


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