Faculty Profiles - FUJII Keisuke

Papers - FUJII Keisuke

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Match Outcome Prediction in 3x3 Basketball: Validating Team Performance Index and Benchmarking Against Shooting Efficiency and Shooting Value (Paris 2024 Olympics) Reviewed Open Access

Michal Nowak, Michal Skalik, Mikolaj Wawrzynczak, Radoslaw Ciejpa, Keisuke Fujii, Jakub Wieckowski

PHYSICAL ACTIVITY REVIEW Vol. 14 ( 1 ) page： 121 - 140 2026.1

Study on a Resilient Helping Role Based on Top-down and Bottom-up Processing in Coordinated Behavior of a Triad Reviewed Open Access

Jun ICHIKAWA, Kazushi TSUTSUI, Keisuke FUJII

IEICE Transactions on Information and Systems Vol. advpub ( 0 ) 2025.12

Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：Institute of Electronics, Information and Communications Engineers (IEICE)

<p>A group distributes roles to achieve a common goal, enabling higher task performance than when doing alone. Such coordination has been investigated in various research fields. These findings suggest that two types of information processing work for efficient and adaptable behaviors: (1) top-down processing established by structured internal knowledge and representation of a group goal, task constraints, plans, and roles, and (2) bottom-up processing based on sensory inputs, such as the generation of flexible movement itself through perception. However, coordination mechanisms have not been fully discussed in terms of the two types of processing. Meanwhile, a previous cognitive science study identified a crucial role for coordination. In a coordinated drawing task, a participant triad shares heterogeneous roles and changes each tension using a reel to move a pen connected to three threads to draw an equilateral triangle. The results indicated that a resilient helping role, which moderately intervenes with other roles to adjust the whole balance according to situations, was related to high team performance. Although this role is not only required for the experimental task, it has not been explained in related work. Considering the aforementioned discussions, the adjustment process particularly involves the two types of processing; however, there is room for further investigation. This study introduced computer simulation to the coordinated drawing task and examined the resilient helping role, using deep reinforcement learning and rule-based modeling. The results showed that an agent with an interactive relationship model in which top-down processing drives bottom-up processing was able to adjust and correct the pen trajectory at the proper timing. Additionally, the deep reinforcement learning and rule-based condition in the adjusting role achieved higher team performance (smaller pen deviation) than the rule-based alone and random conditions. This study supplements the experimental findings and contributes to a constructive understanding of coordination.</p>

DOI： 10.1587/transinf.2025hcp0002

Open Access

CiNii Research

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TrackID3x3: A Dataset and Algorithm for Multi-Player Tracking with Identification and Pose Estimation in 3x3 Basketball Full-court Videos Reviewed Open Access

Kazuhiro Yamada, Li Yin, Qingrui Hu, Ning Ding, Shunsuke Iwashita, Jun Ichikawa, Kiwamu Kotani, Calvin Yeung, Keisuke Fujii

Proceedings of the 8th International ACM Workshop on Multimedia Content Analysis in Sports page： 163 - 173 2025.10

A Generalized Valuation Method for Team Defense by Estimating Probabilities in Football Games. Reviewed

Rikuhei Umemoto, Kazushi Tsutsui, Keisuke Fujii

icSPORTS page： 79 - 89 2025.10

AthleticsPose: Authentic Sports Motion Dataset on Athletic Field and Evaluation of Monocular 3D Pose Estimation Ability Reviewed Open Access

Tomohiro Suzuki, Ryota Tanaka, Calvin Yeung, Keisuke Fujii

8TH INTERNATIONAL ACM WORKSHOP ON MULTIMEDIA CONTENT ANALYSIS IN SPORTS, MMSPORTS 2025 page： 8 - 17 2025.10

TAL3×3: A Dataset and Algorithm for Temporal Action Localization in 3×3 Basketball. Reviewed Open Access

Qingrui Hu, Calvin Yeung, Li Yin, Keisuke Fujii

IEEE Access Vol. 13 page： 186474 - 186487 2025.10

Shot2Tactic-Caption: Multi-Scale Captioning of Badminton Videos for Tactical Understanding Reviewed Open Access

Ning Ding, Keisuke Fujii, Toru Tamaki

8TH INTERNATIONAL ACM WORKSHOP ON MULTIMEDIA CONTENT ANALYSIS IN SPORTS, MMSPORTS 2025 page： 105 - 113 2025.10

Play Evaluation Based on Predicting the Outcome of Back-Row Attacks in Volleyball. Reviewed

Hikaru Yoshihara, Ning Ding, Keisuke Fujii

icSPORTS page： 29 - 37 2025.10

KASportsFormer: Kinematic Anatomy Enhanced Transformer for 3D Human Pose Estimation on Short Sports Scene Video Reviewed Open Access

Zhuoer Yin, Calvin Yeung, Tomohiro Suzuki, Ryota Tanaka, Keisuke Fujii

8TH INTERNATIONAL ACM WORKSHOP ON MULTIMEDIA CONTENT ANALYSIS IN SPORTS, MMSPORTS 2025 page： 95 - 104 2025.10

From Concept to Representation: Modeling Driving Capability and Task Demand with a Multimodal Large Language Model Reviewed Open Access

Haoran Zhou, Alexander Carballo, Keisuke Fujii, Kazuya Takeda

SENSORS Vol. 25 ( 18 ) 2025.9

Space evaluation at the starting point of soccer transitions. Reviewed

Yohei Ogawa, Rikuhei Umemoto, Keisuke Fujii

12th Workshop on Machine Learning and Data Mining for Sports Analytics 2025(MLSA'25) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD'25) 2025.9

Evaluating Movement Initiation Timing in Ultimate Frisbee via Temporal Counterfactuals. Reviewed

Shunsuke Iwashita, Ning Ding, Keisuke Fujii

OpenSTARLab: open approach for spatio-temporal agent data analysis in soccer Reviewed Open Access

Calvin Yeung, Kenjiro Ide, Taiga Someya, Keisuke Fujii

COMPLEX & INTELLIGENT SYSTEMS Vol. 11 ( 8 ) 2025.8

Foul prediction with estimated poses from soccer broadcast video Reviewed Open Access

Jiale Fang, Calvin Yeung, Keisuke Fujii

SPORTS ENGINEERING Vol. 28 ( 33 ) 2025.7

A Zero-Shot LLM Framework for Automated Assignment Grading in Introductory Higher Education Courses Reviewed

Calvin Yeung, Jeff Yu, King Chau Cheung, Tat Wing Wong, Chun Man Chan, Kin Chi Wong, Keisuke Fujii

ARTIFICIAL INTELLIGENCE IN EDUCATION: POSTERS AND LATE BREAKING RESULTS, WORKSHOPS AND TUTORIALS, INDUSTRY AND INNOVATION TRACKS, PRACTITIONERS, DOCTORAL CONSORTIUM, BLUE SKY, AND WIDEAIED, AIED 2025, PT II Vol. 2591 page： 19 - 27 2025.7

AthletePose3D: A Benchmark Dataset for 3D Human Pose Estimation and Kinematic Validation in Athletic Movements. Reviewed

Calvin Yeung, Tomohiro Suzuki, Ryota Tanaka, Zhuoer Yin, Keisuke Fujii

11th International Workshop on Computer Vision in Sports (CVsports) at Conference on Computer Vision and Pattern Recognition (CVPR' 25) page： 5945 - 5956 2025.6

Analyzing coordinated group behavior through role-sharing: a pilot study in female 3-on-3 basketball with practical application. Reviewed International journal Open Access

Jun Ichikawa, Masatoshi Yamada, Keisuke Fujii

Frontiers in sports and active living Vol. 7 page： 1513982 - 1513982 2025.5

Transformer-based neural marked spatio temporal point process model for analyzing football match events. Reviewed Open Access

Calvin Yeung, Tony Sit, Keisuke Fujii

Applied Intelligence Vol. 55 ( 5 ) page： 335 - 335 2025.4

DUIncoder: Learning to Detect Driving Under the Influence Behaviors from Various Normal Driving Data. Reviewed International journal Open Access

Haoran Zhou, Alexander Carballo, Masaki Yamaoka, Minori Yamataka, Keisuke Fujii, Kazuya Takeda

Sensors (Basel, Switzerland) Vol. 25 ( 6 ) 2025.3

Two clusterings to capture basketball players’ shooting tendencies using tracking data: clustering of shooting styles and the shots themselves Reviewed

Kazuhiro Yamada, Keisuke Fujii

International Journal of Computer Science in Sport Vol. 24 ( 1 ) page： 35 - 55 2025.2

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Walter de Gruyter GmbH

Abstract

Studies to understand the shooting preferences of basketball players relied exclusively on data on shot location, which did not lead to concrete understandings because they contained no information on how they moved to that location. Therefore, this study tried to cluster the players’ shooting tendencies using the tracking data of the players’ movements during the game. To do this, we first created hand-crafted shot features that included information on the pre-shot movement. Using those features, the dissimilarity of shooting tendencies between players was computed by considering the shot set of each player as a probability distribution and calculating the Wasserstein distance between them. The clustering based on their dissimilarity resulted in more clusters than in previous studies and allowed for specific shooting styles to be defined. Clustering using Gower distance as a dissimilarity measure for shot features, including a categorical feature, extracted clusters of shots that are useful for understanding players’ more detailed shooting tendencies. These results prove that it is not only the shot location but also how the player moved before the shot that is important to capture the player’s shooting preferences.

DOI： 10.2478/ijcss-2025-0003

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Papers - FUJII Keisuke