Cricket Scene Analysis Using the RetinaNet Architecture

Tevin Moodley, Dustin van der Haar

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


The increased challenges surrounding object detection within the sporting environment have been studied with multiple proposed solutions for various domains. The increasing environmental changes with moving objects, actors, and overlapping objects that are present in sporting video footage make detecting and classifying different objects challenging. However, with the introduction of deep learning, researchers now have the available methods that can learn semantic, high level, and deeper features that can be used to solve problem areas within existing research. Cricket is a sporting domain that exhibits many of these challenges with multiple moving actors and objects. This research paper implements RetinaNet architecture to detect and classify multiple objects within a scene. Six different objects/classes are addressed: fielder, batsman, non-striker, bowler, umpire, ball, and wicket-keeper. Following the dataset preparation, using transfer learning, the images are trained on the RetinaNet architecture, and the architecture proved to be successful by producing a mean average precision score of 86.78%. The trained model manages class precision scores all above 98% except that of the ball class. Upon further investigation, the poor performance of the ball class is due to occlusion and the ball’s small size relative to the overall frame. The proposed model can successfully detect and classify the different objects/classes within a cricket scene and serves as a promising foundation for further research within the cricketing domain.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 25th Iberoamerican Congress, CIARP 2021, Revised Selected Papers
EditorsJoão Manuel Tavares, João Paulo Papa, Manuel González Hidalgo
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages10
ISBN (Print)9783030934194
Publication statusPublished - 2021
Event25th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2021 - Virtual, Online
Duration: 10 May 202113 May 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12702 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference25th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2021
CityVirtual, Online

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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