Recognizing a Sequence of Events from Tennis Video Clips: Addressing Timestep Identification and Subtle Class Differences
File version
Author(s)
Guo, J
Wang, M
Wang, R
Jiang, K
Dong, JS
Griffith University Author(s)
Primary Supervisor
Other Supervisors
Editor(s)
Date
Size
File type(s)
Location
Singapore, Singapore
License
Abstract
Detecting temporally precise and fine-grained events from tennis videos is important in automatic video annotation. This paper addresses the challenges of recognizing a sequence of events from tennis video clips, focusing on accurate timestep identification and distinguishing subtle class differences. We propose a novel but simple end-to-end event detection network to accurately detect and identify the key events, which can be trained on a single GPU. We demonstrate that our model outperforms the existing baselines on our fine-grained tennis event dataset. The research contributes to the development of tennis video analytics and has broader implications in other sports domains.
Journal Title
Conference Title
2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC)
Book Title
Edition
Volume
Issue
Thesis Type
Degree Program
School
Publisher link
Patent number
Funder(s)
Grant identifier(s)
Rights Statement
Rights Statement
Item Access Status
Note
Access the data
Related item(s)
Subject
Machine learning
Applied computing
Persistent link to this record
Citation
Liu, Z; Guo, J; Wang, M; Wang, R; Jiang, K; Dong, JS, Recognizing a Sequence of Events from Tennis Video Clips: Addressing Timestep Identification and Subtle Class Differences, 2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC), 2023, pp. 337-341