Deep learning for classification of cricket umpire postures

Wj Samaraweera, Sc Premaratne, At Dharmaratne

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)


Among various multimedia resources on Internet, videos are the most popular among humans since they require less active brain power compared with static images, text and audio. With the exponential growth of videos available online, solutions for automatic content analysis in different contexts have attracted the attention of researchers. In diverse types of videos, generating highlights of sports videos is a highly considered segment by recent researchers. This research is focused on examining the existing deep learning architectures to analyze the feasibility of combining prevailing network models with classifiers while checking which combination gives the highest performance. A novel combination is presented to automatically detect the cricket umpire postures under five events (Six, No Ball, Out, Wide, None) using Convolutional Neural Networks (CNNs) and standard classifiers. The proposed method utilizes VGG16, ResNet50V2 and MobileNetV2 CNN architectures to extract the features and SVM and Naïve Bayes to classify the identified features into umpire postures. A new dataset has been constructed for this research which contains a total of 350 cricket umpire images. The results demonstrate that ResNet50V2 model in combination with SVM classifier gives the highest classification performance for the proposed dataset.

Original languageEnglish
Title of host publicationNeural Information Processing - 27th International Conference, ICONIP 2020 Bangkok, Thailand, November 18–22, 2020 Proceedings, Part V
EditorsHaiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King
Place of PublicationCham Switzerland
Number of pages8
ISBN (Electronic)9783030638238
ISBN (Print)9783030638221
Publication statusPublished - 2020
EventInternational Conference on Neural Information Processing 2020 - Bangkok, Thailand
Duration: 18 Nov 202022 Nov 2020
Conference number: 27th (Proceedings)

Publication series

NameCommunications in Computer and Information Science
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


ConferenceInternational Conference on Neural Information Processing 2020
Abbreviated titleICONIP 2020
Internet address


  • CNN
  • Image classification
  • Umpire postures

Cite this