Recognizing a Sequence of Events from Tennis Video Clips: Addressing Timestep Identification and Subtle Class Differences

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Liu, Z
Guo, J
Wang, M
Wang, R
Jiang, K
Dong, JS
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2023
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Singapore, Singapore

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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.

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2023 IEEE 28th Pacific Rim International Symposium on Dependable Computing (PRDC)

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Machine learning

Applied computing

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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