研究者詳細 - 河口　信夫

論文 - 河口　信夫

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

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

Journal of Information Processing Vol.33 巻 ( 2025 ) 2025年3月

Life Pattern-Based Human Attribute Estimation Using Only GPS Data 査読有り国際誌 Open Access

Shoji, K; Terashima, H; Tamura, N; Katayama, S; Urano, K; Yonezawa, T; Kawaguchi, N

IEEE ACCESS 13 巻頁： 131803 - 131822 2025年7月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：IEEE Access

Analysis of large-scale GPS data enables the summarization and structuring of human mobility characteristics, providing insights into residents' behavior patterns. However, existing methods analyze sequences of individual-derived stay labels, such as home or workplace, which do not accurately reflect actual area functions and can fail to distinguish individuals based on differences in workplaces. This limitation arises because annotating stay locations and purposes in GPS data is challenging due to the complexity of urban structures and constraints on data quality. In this study, we propose ArLPH, a framework that takes only GPS data as input, models mobility from the perspective of 'when and what types of places he/she visited,' and enables the interpretation of life patterns. ArLPH consists of two modules: 'Area Modeling,' which represents urban areas based on usage patterns, and 'Human Modeling,' which represents individuals based on behavior patterns. By the processing flow of area modeling to human modeling, it becomes possible to analyze human mobility while considering area characteristics. Through an evaluation of a large-scale, real-world GPS dataset collected from tens of thousands of smartphone users, we demonstrate that ArLPH enables the characterization of individuals based on their life patterns and estimates attributes such as occupation (e.g., office workers or homemakers) and lifestyle (e.g., those who work frequently on weekends).

DOI： 10.1109/ACCESS.2025.3591811

Open Access

Web of Science

Scopus

その他リンク： https://ieeexplore.ieee.org/document/11091313

SemantiPack: An Efficient Real-World Data Compressor Using Structural and Semantic Metadata Open Access

Nagata, Y; Kohama, D; Watanabe, Y; Katayama, S; Urano, K; Yonezawa, T; Kawaguchi, N

IEEE ACCESS 13 巻頁： 114159 - 114178 2025年6月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：IEEE Access

The exponential growth of Real-World Data (RWD), primarily collected from IoT sensors and spanning domains such as mobility, environment, and energy consumption, presents critical challenges due to its scale, heterogeneity, and structural variability. Traditional compression methods often fail to adapt efficiently to these complexities, leading to sub-optimal storage and analysis performance. Additionally, while several metadata schemas exist to enhance the availability of RWD, smaller organizations often lack the resources to create and manage metadata effectively. This paper introduces RWD Profile, an automatically generatable metadata schema for RWD, and SemantiPack, which applies tailored compression to data fields in the individual RWD based on RWD Profile. RWD Profile has two kinds of metadata, Structural Profile and Semantic Profile. Structural Profile is generated through rule-based systems, while Semantic Profile is generated using large language models (LLMs) to capture semantic data properties. On the other hand, SemantiPack performs compression at the data field level in RWD using RWD Profile to achieve higher compression ratios. Experimental results demonstrate up to 23.2% improved compression rate for JSON and 19.3% for CSV compared to conventional methods while maintaining a faster or equivalent processing speed compared to conventional methods. Furthermore, SemantiPack supports lossy compression for applications prioritizing storage over precision. This research not only improves compression efficiency but also establishes a scalable solution for automated analysis and sustainable data utilization, paving the way for advancements in RWD management.

DOI： 10.1109/ACCESS.2025.3583829

Open Access

Web of Science

Scopus

Semi-Automated Framework for Digitalizing Multi-Product Warehouses with Large Scale Camera Arrays

Higashiura, K; Yokoyama, K; Asai, Y; Shimosato, H; Kano, K; Katayama, S; Urano, K; Yonezawa, T; Kawaguchi, N

2024 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS, PERCOM 頁： 98 - 105 2024年3月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：2024 IEEE International Conference on Pervasive Computing and Communications, PerCom 2024

As global demand for logistics continues to grow, optimizing the automation and efficiency of distribution warehouse operations is of paramount importance. Digitalizing warehouse environments, which refers to the process of sensing the physical space and extracting meaningful information from the obtained data, offers a promising solution to this challenge. However, converting raw warehouse data, such as video footage captured inside the warehouse, into actionable metadata (e.g., tracking the movement paths of workers and products or analyzing the usage patterns of different warehouse locations) often necessitates significant human intervention. The rise of machine learning further complicates this, as it requires the manual preparation of extensive training datasets. In this paper, we introduce a framework that semi-automates the digitalization process in complex warehouse settings. This framework employs dense optical flow and representation learning to autonomously segment warehouse objects and cluster similar objects, thereby substantially cutting down on annotation costs. To evaluate our approach, we constructed a large-scale data collection platform with over 60 fixed cameras in a real-world logistics warehouse, and the video data from this platform was then applied to our framework. Our evaluations indicate that our method markedly reduces both the time and resources required for warehouse digitalization using the captured video data.

DOI： 10.1109/PERCOM59722.2024.10494498

Web of Science

Scopus

都市を対象とした大規模移動履歴に基づく疑似人流データ生成手法招待有り査読有り Open Access

田村直樹, 浦野健太, 青木俊介, 米澤拓郎, 河口信夫

情報処理学会論文誌 64 巻 ( 1 ) 頁： 123 - 133 2023年1月

ER-Chat: A Text-to-Text Open-Domain Dialogue Framework for Emotion Regulation 査読有り

Katayama Shin, Aoki Shunsuke, Yonezawa Takuro, Okoshi Tadashi, Nakazawa Jin, Kawaguchi Nobuo

IEEE TRANSACTIONS ON AFFECTIVE COMPUTING 13 巻 ( 4 ) 頁： 2229 - 2237 2022年10月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：IEEE Transactions on Affective Computing

Emotions are essential for constructing social relationships between humans and interactive systems. Although emotional and empathetic dialogue generation methods have been proposed for dialogue systems, appropriate dialogue involves not only mirroring emotions and always being empathetic but also complex factors such as context. This paper proposes Emotion Regulation Chat (ER-Chat) as an end-to-end dialogue framework for emotion regulation. Emotion regulation is concerned with actions to approach appropriate emotional states. Learning appropriate emotion and intent when responding on the basis of the context of the dialogue enables the generation of more human-like dialogue. We conducted automatic and human evaluations to demonstrate the superiority of ER-Chat over the baseline system. The results show that inclusion of emotion and intent prediction mechanisms enable generation of dialogues with greater fluency, diversity, emotion awareness, and emotion appropriateness, which are greatly preferred by humans.

DOI： 10.1109/TAFFC.2022.3191973

Web of Science

Scopus

Deep Learning for Ballroom Dance Recognition: A Temporal and Trajectory-Aware Classification Model With Three-Dimensional Pose Estimation and Wearable Sensing 査読有り

Matsuyama Hitoshi, Aoki Shunsuke, Yonezawa Takuro, Hiroi Kei, Kaji Katsuhiko, Kawaguchi Nobuo

IEEE SENSORS JOURNAL 21 巻 ( 22 ) 頁： 25437 - 25448 2021年11月

担当区分：最終著者記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：IEEE Sensors Journal

Dance performance recognition methods have been investigated and shown various applications such as picture-pose evaluation and synchronizing foot timing and direction. However, detailed analysis and feedback are still missing. To provide them, understanding the performance by component level is necessary. Specifically, we formulate it as a dance-figure classification problem using three-dimensional body joints and wearable sensors. Our model is based on long short-term memory (LSTM) and includes the temporal and trajectory-wise structure that uses the trajectory information in a timestep and the temporal masking module. As a result, we achieved 93% accuracy with our proposed method, which is highly overwhelming the baseline result (84.7%) and very close to the accuracy of the experienced dancers (93.6%). We have made the dataset of ballroom dance performance dataset open to researchers to develop the activity recognition field further.

DOI： 10.1109/JSEN.2021.3098744

Web of Science

Scopus

大規模ユーザの滞在情報に基づくエリアの特徴付けとCOVID-19による影響分析査読有り Open Access

庄子和之, 青木俊介, 米澤拓郎, 河口信夫

情報処理学会論文誌 62 巻頁： 1644 - 1657 2021年10月

Digitization Methods for a Logistics Warehouse Towards Digital Twin-Driven Optimization

Kawaguchi N., Asai Y., Kano K., Takaki K., Mori Y., Suzuki Y., Watanabe K., Gushi Y., Katayama S., Urano K., Yonezawa T., Hashiguchi S.

Proceedings 2025 IEEE International Conference on Smart Computing Smartcomp 2025 頁： 186 - 193 2025年6月

記述言語：英語掲載種別：研究論文（学術雑誌）出版者・発行元：Proceedings 2025 IEEE International Conference on Smart Computing Smartcomp 2025

The rapid growth of e-commerce, rising consumer expectations, and labor shortages in Japan pose challenges for logistics warehouse optimization. While automation has advanced outbound operations, inbound processes remain inefficient. This study presents a comprehensive approach to digitizing and optimizing large-scale logistics warehouses, with a case study at TRUSCO NAKAYAMA Corp. in partnership with Nagoya University. Key technologies include a large-scale camera array, multi-camera object tracking, and smartphone-based task estimation. Our contributions include real-time tracking of personnel and packages, cooperative annotation for improved object recognition, and synthetic data augmentation. Additionally, truck berth analysis and indoor localization enhance operational efficiency. To optimize worker shifts and warehouse layout, we apply Factorization Machine Quantum Annealing (FMQA), achieving a 37.4% reduction in lead times and a 14.3% decrease in labor hours. A visualization tool enables warehouse operators to make data-driven decisions. This research demonstrates the potential of digital transformation in logistics and provides a scalable framework for broader industry adoption.

DOI： 10.1109/SMARTCOMP65954.2025.00035

Scopus

その他リンク： https://db.uclab.jp/show/1538

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

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

IEEE Consumer Communications & Networking Conference 2025 2025年1月

Efficient Edge AI Based Annotation and Detection Framework for Logistics Warehouses

Mori Y., Asai Y., Higashiura K., Katayama S., Urano K., Yonezawa T., Kawaguchi N.

Proceedings IEEE Consumer Communications and Networking Conference Ccnc 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：Proceedings IEEE Consumer Communications and Networking Conference Ccnc

As global logistics demand increases, improving the efficiency of warehouse operations has become critical. To achieve this, it's crucial to identify inefficient tasks and layouts by recognizing various warehouse conditions. To address these challenges, we've constructed a large-scale camera infrastructure to convert object positions and movements into data. However, transmitting all video data to the cloud results in significant data transmission and power consumption. Edge AI cameras analyze and extract video data locally, transmitting only essential information and significantly reducing them. Edge AI cameras require a low-computation, high-accuracy object detection model due to limited computational power and complex warehouse environments. Furthermore, the same object appears differently based on the camera's position and angle. Therefore, customizing models for each camera improves accuracy, but the annotation cost would be very high. In this study, we propose a method to perform part of the training data generation on the camera, reducing data transmission and improving annotation efficiency.

DOI： 10.1109/CCNC54725.2025.10976142

Scopus

Discovering Emerging Urban Hotspots Using Satellite Imagery and Human Mobility Data

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

Lecture Notes in Computer Science 15803 LNCS 巻頁： 110 - 120 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：Lecture Notes in Computer Science

Identifying and prioritizing newly emerging points of interest in urban areas is critical for enhancing citizens’ exploration and engagement with their cities. However, traditional discovery channels—such as local media or social networks—often fail to capture real-time urban transformations comprehensively. In this paper, we propose a data-driven methodology that integrates satellite-based change detection with human mobility analytics to reveal, rank, and recommend newly formed or transformed urban hotspots. Specifically, we employ Synthetic Aperture Radar (SAR) Rapid and EAsy Change detection in radar TIme-series by Variation coefficient (REACTIV) to detect structural changes in satellite imagery, and we combine these insights with Area2Vec embeddings derived from BlogWatcher data (2019–2024) to measure shifts in human movement patterns. Our experiments on the rapidly evolving city of Nagoya demonstrate a strong correlation between physical urban transformations and mobility changes, enabling us to pinpoint and prioritize locations with the highest potential appeal. This approach has broad applications for city residents, tourists, businesses, and urban planners by facilitating more informed and timely exploration of dynamic urban spaces.

DOI： 10.1007/978-3-031-92980-9_7

Scopus

Additive Compositionality in Urban Area Embeddings Based on Human Mobility Patterns

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

32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024 頁： 577 - 580 2024年10月

記述言語：英語掲載種別：研究論文（国際会議プロシーディングス）出版者・発行元：32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024

Understanding the characteristics of various urban areas is crucial for applications such as urban planning, tourism policies, market analysis, and infection control. Techniques for embedding areas as vectors in a latent space based on human mobility patterns are actively researched. Many of these area embedding methods define areas as points, grids, or polygons on a geospatial plane and then embed them. However, existing methods do not allow for mutual transformation between these forms and sizes after the initial embedding. Additionally, if the characteristics of an area change due to events such as the opening of new buildings, re-embedding is necessary. Meanwhile, the Word2Vec technique, a representative word embedding method, has a property called additive compositionality. This property allows for the arithmetic operation of word meanings through the arithmetic operations of word embeddings. In this paper, we propose a method to apply this property to existing area embedding techniques, leveraging it for practical tasks such as area shape transformation and searching for areas with trends change.

DOI： 10.1145/3678717.3691279

Scopus

researchmap

Unveiling Human Attributes through Life Pattern Clustering using GPS Data Only

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

32nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL 2024 頁： 621 - 624 2024年10月

Clustering people by their life patterns is valuable in government and business fields. Existing studies often rely on semantic data such as Point of Interest or stay purpose. However, they have the problem that obtaining large datasets is difficult due to the need for annotation work. Some studies try to use only location data. However, they do not reveal the semantics of the area where visitors stay because they only label visited areas by significance according to duration and frequency of stay. In this paper, we propose a framework, LPSeL, for clustering people’s Life Patterns at a Semantic Level using only raw GPS location data. LPSeL is based on the idea that analyzing human mobility first requires understanding urban space. Therefore, it begins with area modeling, which models areas in a city based on people’s activities. Then, treating human mobility as a sequence of area representations makes it possible to model individuals by semantic-level characteristics of their life patterns. We showed that LPSeL is capable of estimating people’s attributes from their life patterns using a real-world dataset consisting of GPS data collected from tens of thousands of smartphone users.

DOI： 10.1145/3678717.3691309

Scopus

researchmap

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.

32nd ACM SIGSPATIAL International Workshop on the Human Mobility Prediction Challenge, HuMob-Challenge 2024 頁： 5 - 9 2024年10月

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招待有り査読有り Open Access

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 65 巻頁： 1511 - 1522 2024年10月

倉庫内業務の量子アニーリングによる最適化とシミュレーションによる評価

瀧上昂希，福島悠人，浅井悠佑，片山晋，浦野健太，米澤拓郎，河口信夫

第81回UBI研究会 2024年2月

Area Modeling using Stay Information for Large-Scale Users and Analysis for Influence of COVID-19.

Kazuyuki Shoji, Shunsuke Aoki, Takuro Yonezawa, Nobuo Kawaguchi

arxiv.org/abs/2401.10648v1 abs/2401.10648 巻 2024年

Balancing Excitement and Environmental Conservation in Information Dissemination for Safari Game Drives

Shimura, K; Katayama, S; Urano, K; Leleito, E; Yonezawa, T; Kawaguchi, N

DISTRIBUTED, AMBIENT AND PERVASIVE INTERACTIONS, PT I, DAPI 2024 14718 巻頁： 311 - 323 2024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：Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

In the context of Kenyan Safari Game Drives, which traditionally rely on visual scouting and intermittent communication, challenges arise regarding the effectiveness of animal detection and environmental conservation. Consequently, animals experience stress, and tourists encounter difficulties in observation due to obstruction by other visitors. Addressing these challenges involves maintaining animal welfare while enhancing tourist satisfaction. To revitalize post-pandemic tourism and enrich safari experiences, this study proposes a wildlife spotting and sharing system. The proposed method integrates wildlife detection and sharing technologies guided by three core principles: preserving encounter serendipity, regulating information access, and promoting environmental conservation. The system architecture incorporates sensors, GPS, and tablet terminals to facilitate real-time data collection, animal identification, and information dissemination. This approach aims to foster an engaging, efficient, and environmentally conscious wildlife spotting and sharing experience, potentially establishing a model for sustainable wildlife tourism in comparable wildlife-rich regions.

DOI： 10.1007/978-3-031-59988-0_19

Web of Science

Scopus

Towards a Real-Time and Energy-Efficient Edge AI Camera Architecture in Mega Warehouse Environment

Asai Y., Mori Y., Higashiura K., Yokoyama K., Katayama S., Urano K., Yonezawa T., Kawaguchi N.

Proceedings - 2024 IEEE 3rd Real-Time and Intelligent Edge Computing Workshop, RAGE 2024 頁： 12 - 17 2024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：Proceedings - 2024 IEEE 3rd Real-Time and Intelligent Edge Computing Workshop, RAGE 2024

In response to the exponential growth of e-commerce, our paper addresses the transformation of warehouse operations towards mega warehouses, necessitating advanced digitalization. We focus on the integration and optimization of edge AI technologies to enhance operational efficiency, accuracy, and timeliness in logistic warehouses. We propose a novel edge AI architecture tailored to the processes of warehouse operations ensuring that digitalization aligns with operational workflows. Utilizing a real-world warehouse equipped with over 60 cameras as a testbed, we demonstrate the practical application and benefits of edge computing in logistics. Our experiments have shown significant potential improvements in energy efficiency and timeliness, crucial metrics for the successful integration of edge AI technologies in warehouses.

DOI： 10.1109/RAGE62451.2024.00010

Scopus

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