研究者詳細 - 米澤　拓郎

論文 - 米澤　拓郎

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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年2月

Efficient Edge AI based Annotation and Detection Framework for Logistics Warehouses 査読有り国際誌

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

IEEE Consumer Communications & Networking Conference 2025 2025年1月

Fixture-aware Panoramic Ceiling Image Synthesis Using an Omnidirectional Camera 査読有り

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

Journal of Information Processing 33 巻 ( 0 ) 頁： 245 - 258 2025年

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Information Processing Society of Japan

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.

DOI： 10.2197/ipsjjip.33.245

CiNii Research

Time-series Stay Frequency for Multi-City Next Location Prediction using Multiple BERTs 査読有り

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 頁： 5 - 9 2024年12月

記述言語：日本語掲載種別：研究論文（学術雑誌）出版者・発行元：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.

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論文 - 米澤 拓郎

<米澤　拓郎>

論文 - 米澤　拓郎