Faculty Profiles - YONEZAWA Takuro

Papers - YONEZAWA Takuro

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VR180 遠隔ロボットアームシステムにおける操作支援のための情報提示手法の検討

筒井秀斗, 渡邉企章, 宇佐美龍斗, 具志祐希, 渡辺圭貴, 林田望海, 志村魁哉, 浦野健太, 米澤拓郎, 河口信夫

第85回UBI研究会 2025.2

Efficient Edge AI based Annotation and Detection Framework for Logistics Warehouses Reviewed International journal

Yuki Mori, Yusuke Asai, Keisuke Higashiura, Shin Katayama, Kenta Urano, Takuro Yonezawa, Nobuo Kawaguchi

IEEE Consumer Communications & Networking Conference 2025 2025.1

Fixture-aware Panoramic Ceiling Image Synthesis Using an Omnidirectional Camera Reviewed

Kohama Daiki, Nagata Yoshiteru, Yasutake Kazushige, Katayama Shin, Urano Kenta, Yonezawa Takuro, Kawaguchi Nobuo

Journal of Information Processing Vol. 33 ( 0 ) page： 245 - 258 2025

Language：English Publishing type：Research paper (scientific journal) Publisher：Information Processing Society of Japan

<p>The manual creation of a ceiling plan consumes many human resources to confirm the current state of existing buildings for renovation. Identifying the positions and the types of existing fixtures is crucial for drawing a ceiling plan. Therefore, to assist in drawing a ceiling plan, an efficient method that generates a reliable photorealistic whole ceiling surface image is essential. We present a method of fixture-aware panoramic ceiling image synthesis using an omnidirectional camera. A fixture-aware panoramic ceiling image is a photorealistic image that depicts the whole ceiling surface and no seams on ceiling fixtures for its reliability regarding assistance in drawing a ceiling plan. The proposed method is remarkably effortless because it requires the users to work only 2 procedures for synthesizing the image, 1) shooting an omnidirectional video underneath the ceiling and 2) fixture measurement in the image. In the experiment, we evaluated the resulting image for the positional and shape accuracy of the ceiling fixtures and the time efficiency of the measurement. Our proposed method demonstrated a positional accuracy of 2.25 times higher, a shape accuracy of 5.87 times higher, and a time efficiency of the measurement 11.09 times higher than the baseline.</p>

DOI： 10.2197/ipsjjip.33.245

CiNii Research

Time-series Stay Frequency for Multi-City Next Location Prediction using Multiple BERTs Reviewed

Terashima H., Takagi S., Tamura N., Shoji K., Hossain T., Katayama S., Urano K., Yonezawa T., Kawaguchi N.

2nd ACM SIGSPATIAL International Workshop on the Human Mobility Prediction Challenge, HuMob-Challenge 2024 page： 5 - 9 2024.12

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：2nd ACM SIGSPATIAL International Workshop on the Human Mobility Prediction Challenge, HuMob-Challenge 2024

Human Mobility Prediction Challenge 2024 was organized to compare human future movement prediction methods using a unified dataset. The challenge focuses on human movement prediction in multiple cities with varying numbers of users. Many existing movement prediction methods train deep learning models using large-scale movement histories from cities and make predictions. While cities with large users have sufficient training data, smaller cities with fewer users may face challenges due to insufficient data, raising concerns about lower prediction accuracy. Additionally, it is difficult to treat stay locations between different cities’ movement histories due to differences in spatial area arrangement, making it challenging to share data across cities. To address this issue, we propose human movement prediction method that utilizes time-series stay frequency patterns, which can be commonly applied across different cities. This method demonstrated superiority in predicting movements in cities with fewer users compared to models trained and predicted using only the movement histories of the target city. Furthermore, the method achieved top 10 prediction accuracy in HuMob Challenge 2024.

DOI： 10.1145/3681771.3699909

Scopus

共通潜在空間を介した多様な都市データのクロスモーダル分析手法

田村直樹, 寺島青, 庄子和之, 片山晋, 浦野健太, 米澤拓郎, 河口信夫

第84回UBI研究会 2024.11

時系列滞在頻度傾向と移動傾向を組み合わせた移動先予測手法

寺島青, 髙木峻, 田村直樹, 庄子和之, Tahera Hossain, 片山晋, 浦野健太, 米澤拓郎, 河口信夫

第84回UBI研究会 2024.11

EngagedSync ： MR 共有空間内ユーザの相互状況理解向上のための適応的な周辺環境情報抽出・転送システム

志村魁哉, 加納一馬, Tahera Hossain, 片山晋, 浦野健太, 米澤拓郎, 河口信夫

第84回UBI研究会 2024.11

Additive Compositionality in Urban Area Embeddings Based on Human Mobility Patterns Reviewed

Naoki Tamura, Haru Terashima, Kazuyuki Shoji, Shin Katayama, Kenta Urano, Takuro Yonezawa, Nobuo Kawaguchi

Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems page： 577 - 580 2024.10

Unveiling Human Attributes through Life Pattern Clustering using GPS Data Only Reviewed

Kazuyuki Shoji, Haru Terashima, Nobuo Kawaguchi, Shin Katayama, Kenta Urano, Takuro Yonezawa, Naoki Tamura

Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems page： 621 - 624 2024.10

自律移動ロボットを用いた人流計測のための探索経路生成 Reviewed

下里浩昇, 片山晋, 浦野健太, 米澤拓郎, 河口信夫

情報処理学会論文誌 Vol. 65 ( 10 ) page： 1511 - 1522 2024.10

DiverCityMeter: GPSデータのみを用いた生活パターン多様性から見る COVID-19 の影響分析に関する研究

庄子和之，寺島青，田村直樹，片山晋，浦野健太，米澤拓郎，河口信夫

第112回MBL研究会 2024.9

遠隔MRロボットアーム制御によるスマートな3Dスキャンシステム

宇佐美龍斗、渡邉企章、具志祐希、筒井秀斗、渡辺圭貴、加納一馬、相川雄也、志村魁哉、林田望海、浦野健太、米澤拓郎、河口信夫

第29回日本バーチャルリアリティ学会大会 2024.9

スマートフォン・全方位カメラを用いた作業効率化のための半自動天伏図作成システム

小濱大輝，永田吉輝，安武和成，酒見和幸，片山晋，浦野健太，米澤拓郎，河口信夫

令和6年度空気調和・衛生工学会大会 2024.9

Autoware-in-Forest: 森林環境における丸太運搬フォワーダの自動走行

浦野健太, 米澤拓郎, 河口信夫

第42回日本ロボット学会学術講演会（RSJ2024） 2024.9

3次元点群解析に基づくロボット移動可能領域の半自動認識手法

相川雄也，片山晋，浦野健太，米澤拓郎，河口信夫

第42回日本ロボット学会学術講演会（RSJ2024） 2024.9

エッジ音声処理とCAN通信を用いた多数接続可能な発話方向推定システム

市川　直人，加納　一馬，永田　吉輝，片山　晋，浦野　健太，米澤　拓郎，河口　信夫

電気・電子・情報関係学会東海支部連合大会 2024.8

物流倉庫におけるスマートフォンを用いた作業者のタスク認識

渡邊企章，加納一馬，片山晋，浦野健太，米澤拓郎，河口信夫

電気・電子・情報関係学会東海支部連合大会 2024.8

マルチカメラによるオクルージョン回避を用いた荷物置き場の占有状況検出

具志祐希，加納一馬，片山晋，浦野健太, 米澤拓郎, 河口信夫

電気・電子・情報関係学会東海支部連合大会 2024.8

天伏図自動復元のための設備位置・形状アライメント手法

小濱大輝，永田吉輝，安武和成，片山晋，浦野健太，米澤拓郎，河口信夫

第42回電気設備学会全国大会 2024.8

アノテーションロスを克服するラベル付き画像セグメントの合成によるデータ拡張

加納一馬，森裕輝，東浦圭亮，Tahera HOSSAIN，片山晋，浦野健太，米澤拓郎，河口信夫

画像電子学会年次大会（MCC2024） 2024.8

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Papers - YONEZAWA Takuro