A survey of video-based human action recognition in team sports

Over the past few decades, numerous studies have focused on identifying and recognizing human actions using machine learning and computer vision techniques. Video-based human action recognition (HAR) aims to detect actions from video sequences automatically. This can cover simple gestures to complex...

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Bibliographic Details
Published in:The Artificial intelligence review 2024-09, Vol.57 (11), p.293, Article 293
Main Authors: Yin, Hongwei, Sinnott, Richard O., Jayaputera, Glenn T.
Format: Article
Language:English
Subjects:
Artificial Intelligence
Australian football
Cameras
Classification
Computer Science
Computer vision
Datasets
Deep learning
Football
Human activity recognition
Localization
Machine learning
Moving object recognition
Professional soccer
Sensors
Soccer
Team sports
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Summary:Over the past few decades, numerous studies have focused on identifying and recognizing human actions using machine learning and computer vision techniques. Video-based human action recognition (HAR) aims to detect actions from video sequences automatically. This can cover simple gestures to complex actions involving multiple people interacting with objects. Actions in team sports exhibit a different nature compared to other sports, since they tend to occur at a faster pace and involve more human-human interactions. As a result, research has typically not focused on the challenges of HAR in team sports. This paper comprehensively summarises HAR-related research and applications with specific focus on team sports such as football (soccer), basketball and Australian rules football. Key datasets used for HAR-related team sports research are explored. Finally, common challenges and future work are discussed, and possible research directions identified.
ISSN:1573-7462
0269-2821
1573-7462
DOI:10.1007/s10462-024-10934-9