Annotation Techniques for Judo Combat Phase Classification from Tournament Footage
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909422222049280 |
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| author | Miyaguchi, Anthony Moutahir, Jed Sutar, Tanmay |
| author_facet | Miyaguchi, Anthony Moutahir, Jed Sutar, Tanmay |
| contents | This paper presents a semi-supervised approach to extracting and analyzing combat phases in judo tournaments using live-streamed footage. The objective is to automate the annotation and summarization of live streamed judo matches. We train models that extract relevant entities and classify combat phases from fixed-perspective judo recordings. We employ semi-supervised methods to address limited labeled data in the domain. We build a model of combat phases via transfer learning from a fine-tuned object detector to classify the presence, activity, and standing state of the match. We evaluate our approach on a dataset of 19 thirty-second judo clips, achieving an F1 score on a $20\%$ test hold-out of 0.66, 0.78, and 0.87 for the three classes, respectively. Our results show initial promise for automating more complex information retrieval tasks using rigorous methods with limited labeled data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07155 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Annotation Techniques for Judo Combat Phase Classification from Tournament Footage Miyaguchi, Anthony Moutahir, Jed Sutar, Tanmay Computer Vision and Pattern Recognition Multimedia This paper presents a semi-supervised approach to extracting and analyzing combat phases in judo tournaments using live-streamed footage. The objective is to automate the annotation and summarization of live streamed judo matches. We train models that extract relevant entities and classify combat phases from fixed-perspective judo recordings. We employ semi-supervised methods to address limited labeled data in the domain. We build a model of combat phases via transfer learning from a fine-tuned object detector to classify the presence, activity, and standing state of the match. We evaluate our approach on a dataset of 19 thirty-second judo clips, achieving an F1 score on a $20\%$ test hold-out of 0.66, 0.78, and 0.87 for the three classes, respectively. Our results show initial promise for automating more complex information retrieval tasks using rigorous methods with limited labeled data. |
| title | Annotation Techniques for Judo Combat Phase Classification from Tournament Footage |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2412.07155 |