Sports Field Recognition Using Deep Multi-task Learning

• Item type: Journal(1)
• 公開日: 2021-04-15
• タイトル: Sports Field Recognition Using Deep Multi-task Learning
• タイトル / 言語: en
• タイトル: Sports Field Recognition Using Deep Multi-task Learning
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文（推薦論文）] homography, semantic segmentation, multi-task learning, sports analytics
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: NTT Communications Corporation
• 著者所属(英): en
• 著者所属(英): NTT Communications Corporation
• 著者名: Shuhei, Tarashima
• 著者名(英): Shuhei, Tarashima
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In this paper we propose a novel approach to build a single shot regressor, called SFLNet, that directly predicts a parameter set relating a sports field seen in an input frame to its metric model. This problem is challenging due to the huge intra-class variance of sports fields and the large number of free parameters to be predicted. To address these issues, we propose to train our regressor in combination with semantic segmentation in a multi-task learning framework. We also introduce an additional module to exploit the spacial consistency of sports fields, which boosts both regression and segmentation performances. SFLNet can be trained with a dataset that can be semi-automatically built from human annotated point-to-point correspondences. To our knowledge, this work is the first attempt to solve this sports field localization problem relying only on an end-to-end deep learning framework. Experiments on our new dataset based on basketball games validate our approach over baseline methods.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.328; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In this paper we propose a novel approach to build a single shot regressor, called SFLNet, that directly predicts a parameter set relating a sports field seen in an input frame to its metric model. This problem is challenging due to the huge intra-class variance of sports fields and the large number of free parameters to be predicted. To address these issues, we propose to train our regressor in combination with semantic segmentation in a multi-task learning framework. We also introduce an additional module to exploit the spacial consistency of sports fields, which boosts both regression and segmentation performances. SFLNet can be trained with a dataset that can be semi-automatically built from human annotated point-to-point correspondences. To our knowledge, this work is the first attempt to solve this sports field localization problem relying only on an end-to-end deep learning framework. Experiments on our new dataset based on basketball games validate our approach over baseline methods.; ------------------------------; This is a preprint of an article intended for publication Journal of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.29(2021) (online); DOI http://dx.doi.org/10.2197/ipsjjip.29.328; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 62, 号 4, 発行日 2021-04-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764