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Multimodal Important Scene Detection in Far-view Soccer Videos Based on Single Deep Neural Architecture

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Title: Multimodal Important Scene Detection in Far-view Soccer Videos Based on Single Deep Neural Architecture
Authors: Haruyama, Tomoki Browse this author
Takahashi, Sho Browse this author
Ogawa, Takahiro Browse this author →KAKEN DB
Haseyama, Miki Browse this author →KAKEN DB
Keywords: Semantic video analysis
sports video
deep learning
convolutional neural network
support vector machine
Issue Date: 2020
Publisher: The Institute of Image Information and Television Engineers
Journal Title: ITE Transactions on Media Technology and Applications
Volume: 8
Issue: 2
Start Page: 89
End Page: 99
Publisher DOI: 10.3169/mta.8.89
Abstract: The details of the matches of soccer can be estimated from visual and audio sequences, and they correspond to the occurrence of important scenes. Therefore, the use of these sequences is suitable for important scene detection. In this paper, a new multimodal method for important scene detection from visual and audio sequences in far-view soccer videos based on a single deep neural architecture is presented. A unique point of our method is that multiple classifiers can be realized by a single deep neural architecture that includes a Convolutional Neural Network-based feature extractor and a Support Vector Machine-based classifier. This approach provides a solution to the problem of not being able to simultaneously optimize different multiple deep neural architectures from a small amount of training data. Then we monitor confidence measures output from this architecture for the multimodal data and enable their integration to obtain the final classification result.
Type: article
URI: http://hdl.handle.net/2115/78132
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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