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Preliminary Investigation of End-to-end Indoor Trajectory Prediction Based on 2D Floorplan Image and Graph Neural Network
https://ipsj.ixsq.nii.ac.jp/records/240563
https://ipsj.ixsq.nii.ac.jp/records/2405639d777e6a-3d5e-4e6a-b352-9d40b35f420e
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-HCI24210022.pdf (1.5 MB)
2026年11月11日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥660, IPSJ:学会員：¥330, HCI:会員：¥0, DLIB:会員：¥0 | ||
| Item type | SIG Technical Reports(1) | |||||||||
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| 公開日 | 2024-11-11 | |||||||||
| タイトル | ||||||||||
| タイトル | Preliminary Investigation of End-to-end Indoor Trajectory Prediction Based on 2D Floorplan Image and Graph Neural Network | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Preliminary Investigation of End-to-end Indoor Trajectory Prediction Based on 2D Floorplan Image and Graph Neural Network | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | ウェアラブル | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_18gh | |||||||||
| 資源タイプ | technical report | |||||||||
| 著者所属 | ||||||||||
| Graduate School of Information Science and Technology, Osaka University | ||||||||||
| 著者所属 | ||||||||||
| Graduate School of Information Science and Technology, Osaka University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Information Science and Technology, Osaka University | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Graduate School of Information Science and Technology, Osaka University | ||||||||||
| 著者名 |
Yuqiao, Wang
× Yuqiao, Wang
× Takuya, Maekawa
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| 著者名(英) |
Yuqiao, Wang
× Yuqiao, Wang
× Takuya, Maekawa
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Predicting movement trajectories of individuals within indoor environments is essential for applications such as improving navigation systems and enhancing spatial awareness in smart buildings. Traditional trajectory prediction methods often perform map-matching by leveraging floormap information, resulting in complex and costly systems due to feature extraction from the floormap information and integrating the extracted floormap information into computationally expensive non-linear systems such as particle filters. This study addresses these challenges by introducing a user-friendly, end-to-end model that eliminates the need for complicated pre-processing or specialized data collection. Our approach simplifies the task by using only Inertial Measurement Units (IMU) data and a 2D floorplan image to construct a neural network-based trajectory prediction system. By leveraging Graph Neural Networks (GNNs) to integrate user's positional information into the floor map information and Long Short-Term Memory (LSTM) networks to capture the temporal dynamics of user movements, the model reconstructs the trajectory of the pedestrian based on IMU data. This method offers a practical and accessible solution for accurate trajectory prediction in complex indoor settings by integrating spatial and temporal data into a unified framework, demonstrating the effectiveness of GNNs in processing spatial structures for real-world applications. | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | Predicting movement trajectories of individuals within indoor environments is essential for applications such as improving navigation systems and enhancing spatial awareness in smart buildings. Traditional trajectory prediction methods often perform map-matching by leveraging floormap information, resulting in complex and costly systems due to feature extraction from the floormap information and integrating the extracted floormap information into computationally expensive non-linear systems such as particle filters. This study addresses these challenges by introducing a user-friendly, end-to-end model that eliminates the need for complicated pre-processing or specialized data collection. Our approach simplifies the task by using only Inertial Measurement Units (IMU) data and a 2D floorplan image to construct a neural network-based trajectory prediction system. By leveraging Graph Neural Networks (GNNs) to integrate user's positional information into the floor map information and Long Short-Term Memory (LSTM) networks to capture the temporal dynamics of user movements, the model reconstructs the trajectory of the pedestrian based on IMU data. This method offers a practical and accessible solution for accurate trajectory prediction in complex indoor settings by integrating spatial and temporal data into a unified framework, demonstrating the effectiveness of GNNs in processing spatial structures for real-world applications. | |||||||||
| 書誌レコードID | ||||||||||
| 収録物識別子タイプ | NCID | |||||||||
| 収録物識別子 | AA1221543X | |||||||||
| 書誌情報 |
研究報告ヒューマンコンピュータインタラクション（HCI） 巻 2024-HCI-210, 号 22, p. 1-8, 発行日 2024-11-11 |
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| ISSN | ||||||||||
| 収録物識別子タイプ | ISSN | |||||||||
| 収録物識別子 | 2188-8760 | |||||||||
| Notice | ||||||||||
| SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc. | ||||||||||
| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
