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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2501.16100 |
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| _version_ | 1866929693011214336 |
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| author | Della Santa, Francesco Lalli, Morgana |
| author_facet | Della Santa, Francesco Lalli, Morgana |
| contents | This study presents a novel Deep Learning-based and lightweight approach for the automated detection of sports highlights (HLs) from audio and video sources. HL detection is a key task in sports video analysis, traditionally requiring significant human effort. Our solution leverages Deep Learning (DL) models trained on relatively small datasets of audio Mel-spectrograms and grayscale video frames, achieving promising accuracy rates of 89% and 83% for audio and video detection, respectively. The use of small datasets, combined with simple architectures, demonstrates the practicality of our method for fast and cost-effective deployment. Furthermore, an ensemble model combining both modalities shows improved robustness against false positives and false negatives. The proposed methodology offers a scalable solution for automated HL detection across various types of sports video content, reducing the need for manual intervention. Future work will focus on enhancing model architectures and extending this approach to broader scene-detection tasks in media analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_16100 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Automated Detection of Sport Highlights from Audio and Video Sources Della Santa, Francesco Lalli, Morgana Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning This study presents a novel Deep Learning-based and lightweight approach for the automated detection of sports highlights (HLs) from audio and video sources. HL detection is a key task in sports video analysis, traditionally requiring significant human effort. Our solution leverages Deep Learning (DL) models trained on relatively small datasets of audio Mel-spectrograms and grayscale video frames, achieving promising accuracy rates of 89% and 83% for audio and video detection, respectively. The use of small datasets, combined with simple architectures, demonstrates the practicality of our method for fast and cost-effective deployment. Furthermore, an ensemble model combining both modalities shows improved robustness against false positives and false negatives. The proposed methodology offers a scalable solution for automated HL detection across various types of sports video content, reducing the need for manual intervention. Future work will focus on enhancing model architectures and extending this approach to broader scene-detection tasks in media analysis. |
| title | Automated Detection of Sport Highlights from Audio and Video Sources |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2501.16100 |