BoxingVI: A Multi-Modal Benchmark for Boxing Action Recognition and Localization

Fuente: arXiv
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Main Authors: Kumar, Rahul, Baghel, Vipul, Singh, Sudhanshu, Badatya, Bikash Kumar, Yadav, Shivam, Srinivasan, Babji, Hegde, Ravi
Format: Preprint
Published: 2025
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author Kumar, Rahul
Baghel, Vipul
Singh, Sudhanshu
Badatya, Bikash Kumar
Yadav, Shivam
Srinivasan, Babji
Hegde, Ravi
author_facet Kumar, Rahul
Baghel, Vipul
Singh, Sudhanshu
Badatya, Bikash Kumar
Yadav, Shivam
Srinivasan, Babji
Hegde, Ravi
contents Accurate analysis of combat sports using computer vision has gained traction in recent years, yet the development of robust datasets remains a major bottleneck due to the dynamic, unstructured nature of actions and variations in recording environments. In this work, we present a comprehensive, well-annotated video dataset tailored for punch detection and classification in boxing. The dataset comprises 6,915 high-quality punch clips categorized into six distinct punch types, extracted from 20 publicly available YouTube sparring sessions and involving 18 different athletes. Each clip is manually segmented and labeled to ensure precise temporal boundaries and class consistency, capturing a wide range of motion styles, camera angles, and athlete physiques. This dataset is specifically curated to support research in real-time vision-based action recognition, especially in low-resource and unconstrained environments. By providing a rich benchmark with diverse punch examples, this contribution aims to accelerate progress in movement analysis, automated coaching, and performance assessment within boxing and related domains.
format Preprint
id arxiv_https___arxiv_org_abs_2511_16524
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle BoxingVI: A Multi-Modal Benchmark for Boxing Action Recognition and Localization
Kumar, Rahul
Baghel, Vipul
Singh, Sudhanshu
Badatya, Bikash Kumar
Yadav, Shivam
Srinivasan, Babji
Hegde, Ravi
Computer Vision and Pattern Recognition
Accurate analysis of combat sports using computer vision has gained traction in recent years, yet the development of robust datasets remains a major bottleneck due to the dynamic, unstructured nature of actions and variations in recording environments. In this work, we present a comprehensive, well-annotated video dataset tailored for punch detection and classification in boxing. The dataset comprises 6,915 high-quality punch clips categorized into six distinct punch types, extracted from 20 publicly available YouTube sparring sessions and involving 18 different athletes. Each clip is manually segmented and labeled to ensure precise temporal boundaries and class consistency, capturing a wide range of motion styles, camera angles, and athlete physiques. This dataset is specifically curated to support research in real-time vision-based action recognition, especially in low-resource and unconstrained environments. By providing a rich benchmark with diverse punch examples, this contribution aims to accelerate progress in movement analysis, automated coaching, and performance assessment within boxing and related domains.
title BoxingVI: A Multi-Modal Benchmark for Boxing Action Recognition and Localization
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2511.16524