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HUMAN BEHAVIOR PREDICTION FOR CITYSCAPE IMAGES USING MULTIMODAL DEEP LEARNING
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- ONO Kotaro
- KOZO KEIKAKU ENGINEERING Inc
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- YAMADA Satoshi
- Dept. of Architecture and Urban Design, Ritsumeikan Univ.
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- MUNEMOTO Shinsaku
- Dept. of Architecture and Urban Design, Ritsumeikan Univ.
Bibliographic Information
- Other Title
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- マルチモーダル深層学習を用いた街並み画像に対する人間の振る舞い予測
- - For prediction of gazing tendency and prediction of willingness to visit using multi- channelal data with results -
- -注視点傾向予測及び結果を付与した多次元データによる訪問意欲予測を対象に-
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Description
<p>This study aimed to estimate human willingness to visit cityscape images via artificial intelligence (AI) using multimodal deep learning. In this study, gaze information was acquired through subject experiments using a measurement device. We added gaze information when humans felt motivated to visit the cityscape image, and confirmed whether the estimation accuracy of AI would improve. We also created an AI model that generated gaze-view images, and used it for multimodal deep learning. We used pix2pix to generate the images. Finally, we verified the accuracy of the proposed multimodal deep learning approach, when the generated pseudo-gaze image was attached.</p>
Journal
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- Journal of Architecture and Planning (Transactions of AIJ)
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Journal of Architecture and Planning (Transactions of AIJ) 87 (798), 1602-1611, 2022-08-01
Architectural Institute of Japan
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Details 詳細情報について
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- CRID
- 1390011461952152448
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- ISSN
- 18818161
- 13404210
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- Text Lang
- ja
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- Article Type
- journal article
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- Data Source
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- JaLC
- Crossref
- KAKEN
- OpenAIRE
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- Abstract License Flag
- Disallowed
