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DHMDL: Dynamically Hashed Multimodal Deep Learning Framework for Racket Video Summarization Using Audio and Visual Markers.

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Title: DHMDL: Dynamically Hashed Multimodal Deep Learning Framework for Racket Video Summarization Using Audio and Visual Markers.
Authors: Priyanka, G.1 (AUTHOR) priyanka@mepcoeng.ac.in, Senthil Kumar, J.2 (AUTHOR), Prasha Meena, M.3 (AUTHOR)
Source: Applied Artificial Intelligence. Dec2025, Vol. 39 Issue 1, p1-41. 41p.
Subjects: Video summarization, Racket games, Artificial neural networks, Wimbledon Championships
Geographic Terms: Wimbledon (London, England)
Abstract: Sports videos are being streamed over a large range of social media platforms, and they always have a huge audience base and viewer history. In order to provide more excitement for the users in watching a completed game, automatic video summarization is an inevitable solution. While sports like soccer, cricket have been the main focus of the video summarization research, little attention has been centered over racket sports. Our proposed dynamically hashed multimodal deep learning (DHMDL) sports video summarization framework fuses excitement scores by utilizing deep learning architectures to extract cues from multi modalities namely commentator voice, spectators' cheers and player's expression and then leverages to generate video segment as highlight by using hash codes mapped to weighted sum of excitement score. Also, the proposed synchronized parallel processing ranking based hash map framed using the merge sorting technique for categorizing the excitement scores is applied in video summarization. The framework is tested on U.S. Open and Wimbledon match videos and the results show superior results against state-of-art techniques with normalized discounted cumulative gain (nDCG) score improved by 2%, positive matching highlight segment identification increased by 20% on YouTube Videos. [ABSTRACT FROM AUTHOR]
Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: DHMDL: Dynamically Hashed Multimodal Deep Learning Framework for Racket Video Summarization Using Audio and Visual Markers.
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  Data: <searchLink fieldCode="DE" term="%22Video+summarization%22">Video summarization</searchLink><br /><searchLink fieldCode="DE" term="%22Racket+games%22">Racket games</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Wimbledon+Championships%22">Wimbledon Championships</searchLink>
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  Data: Sports videos are being streamed over a large range of social media platforms, and they always have a huge audience base and viewer history. In order to provide more excitement for the users in watching a completed game, automatic video summarization is an inevitable solution. While sports like soccer, cricket have been the main focus of the video summarization research, little attention has been centered over racket sports. Our proposed dynamically hashed multimodal deep learning (DHMDL) sports video summarization framework fuses excitement scores by utilizing deep learning architectures to extract cues from multi modalities namely commentator voice, spectators' cheers and player's expression and then leverages to generate video segment as highlight by using hash codes mapped to weighted sum of excitement score. Also, the proposed synchronized parallel processing ranking based hash map framed using the merge sorting technique for categorizing the excitement scores is applied in video summarization. The framework is tested on U.S. Open and Wimbledon match videos and the results show superior results against state-of-art techniques with normalized discounted cumulative gain (nDCG) score improved by 2%, positive matching highlight segment identification increased by 20% on YouTube Videos. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Applied Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/08839514.2025.2462382
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      – Code: eng
        Text: English
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      – SubjectFull: Video summarization
        Type: general
      – SubjectFull: Racket games
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Wimbledon Championships
        Type: general
      – SubjectFull: Wimbledon (London, England)
        Type: general
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      – TitleFull: DHMDL: Dynamically Hashed Multimodal Deep Learning Framework for Racket Video Summarization Using Audio and Visual Markers.
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            NameFull: Senthil Kumar, J.
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            NameFull: Prasha Meena, M.
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            – D: 01
              M: 12
              Text: Dec2025
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              Y: 2025
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