Faculty Profiles - FUJII Keisuke

Papers - FUJII Keisuke

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Putting performance bias to the front-lower side of the hole on steep slopes: Differences in strategies and movements between professional and amateur golfers Reviewed

Yumiko Hasegawa, Ayako Okada, Keisuke Fujii

PLOS ONE Vol. 19 ( 12 ) page： e0314820 - e0314820 2024.12

Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：Public Library of Science (PLoS)

When playing on an sloped golf course, the ball often misses the hole to the front-lower side, which is also referred to as the “amateur side.” This can be attributed to the difficulty in perceiving the green slope and choosing the appropriate launch direction and ball speed, especially for amateurs. The study had three main objectives. The first was to examine whether a front-down bias toward the hole could be experimentally observed in golfers’ putting. Second, to measure golfers’ aims and movements to determine why golfers, especially amateurs, putt poorly on steep slopes. Third, to investigate using a miniature platform how golfers recognized the steepness of the slopes and the relationship between their aim and their slope recognition. Twelve professionals and twelve intermediate amateurs were asked to perform in the 1- and 3-degree conditions (left-to-right line) at a distance of 3 m from the hole. Participants wore shutter goggles to inhibit visual feedback of outcomes. The aim, address, ball launch direction (analyzed as angles), and kinematics of the putter head and ball were measured. The results of this study experimentally confirmed the amateur side and indicated that the aim, address, and launch directions of the amateurs were smaller than those of the professionals. As for reproducing the slope task, we found that the amateurs underestimated the steepness on both conditions when compared to professionals. Additionally, golfers’ aim and slope recognition were weakly correlated. These suggest that amateurs, compared to professionals, were unable to choose the optimal speed of the putter head and the launch direction of the ball in relation to the slope. Furthermore, amateurs’ recognition was worse than that of professionals, which may affect their aim.

DOI： 10.1371/journal.pone.0314820

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ButterFlySet: A 2D video dataset for pose estimation of the flying butterflies in the wild Reviewed

Kai Amino, Keisuke Fujii

Visual observation and analysis of Vertebrate And Insect Behavior (VAIB) at International Conference on Pattern Recognition (ICPR 2024) 2024.12

Runner re-identification from single-view running video in the open-world setting. Reviewed

Tomohiro Suzuki, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

Multimedia Tools and Applications Vol. 83 ( 36 ) page： 83729 - 83745 2024.11

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Springer Science and Business Media LLC

DOI： 10.1007/s11042-024-18881-x

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Other Link： https://link.springer.com/article/10.1007/s11042-024-18881-x/fulltext.html

Estimation of Overlapped Tactical Actions from Soccer Match Video. Reviewed

Kento Kuroda, Ikuma Uchida, Keisuke Fujii, Yoshinari Kameda

icSPORTS page： 257 - 264 2024.11

Basketball-SORT: an association method for complex multi-object occlusion problems in basketball multi-object tracking. Reviewed

Qingrui Hu, Atom Sco, Calvin Yeung, Keisuke Fujii

Multimedia Tools and Applications Vol. 83 ( 38 ) page： 86281 - 86297 2024.11

MulCPred: Learning Multi-Modal Concepts for Explainable Pedestrian Action Prediction. Reviewed International journal Open Access

Yan Feng, Alexander Carballo, Keisuke Fujii, Robin Karlsson, Ming Ding, Kazuya Takeda

Sensors (Basel, Switzerland) Vol. 24 ( 20 ) page： 6742 2024.10

Language：English Publishing type：Research paper (scientific journal)

Pedestrian action prediction is crucial for many applications such as autonomous driving. However, state-of-the-art methods lack the explainability needed for trustworthy predictions. In this paper, a novel framework called MulCPred is proposed that explains its predictions based on multi-modal concepts represented by training samples. Previous concept-based methods have limitations, including the following: (1) they cannot be directly applied to multi-modal cases; (2) they lack the locality needed to attend to details in the inputs; (3) they are susceptible to mode collapse. These limitations are tackled accordingly through the following approaches: (1) a linear aggregator to integrate the activation results of the concepts into predictions, which associates concepts of different modalities and provides ante hoc explanations of the relevance between the concepts and the predictions; (2) a channel-wise recalibration module that attends to local spatiotemporal regions, which enables the concepts with locality; (3) a feature regularization loss that encourages the concepts to learn diverse patterns. MulCPred is evaluated on multiple datasets and tasks. Both qualitative and quantitative results demonstrate that MulCPred is promising in improving the explainability of pedestrian action prediction without obvious performance degradation. Moreover, by removing unrecognizable concepts, MulCPred shows improved cross-dataset prediction performance, suggesting its potential for further generalization.

DOI： 10.3390/s24206742

Open Access

PubMed

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A strategic framework for optimal decisions in football 1-vs-1 shot-taking situations: an integrated approach of machine learning, theory-based modeling, and game theory Reviewed Open Access

Calvin Yeung, Keisuke Fujii

Complex & Intelligent Systems 2024.10

Evaluating soccer match prediction models: a deep learning approach and feature optimization for gradient-boosted trees. Reviewed Open Access

Calvin Yeung, Rory P. Bunker, Rikuhei Umemoto, Keisuke Fujii

Machine Learning Vol. 113 ( 10 ) page： 7541 - 7564 2024.10

3D Pose-Based Temporal Action Segmentation for Figure Skating: A Fine-Grained and Jump Procedure-Aware Annotation Approach. Reviewed Open Access

Ryota Tanaka, Tomohiro Suzuki, Keisuke Fujii

Proceedings of the 7th ACM International Workshop on Multimedia Content Analysis in Sports(MMSports@MM) page： 17 - 26 2024.10

Multi-agent statistically discriminative sub-trajectory mining and an application to NBA basketball Reviewed

Rory Paul Bunker, Vo Nguyen Le Duy, Yasuo Tabei, Ichiro Takeuchi, Keisuke Fujii

Journal of Quantitative Analysis in Sports 2024.9

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

Abstract

Improvements in tracking technology through optical and computer vision systems have enabled a greater understanding of the movement-based behaviour of multiple agents, including in team sports. In this study, a multi-agent statistically discriminative sub-trajectory mining (MA-Stat-DSM) method is proposed that takes a set of binary-labelled agent trajectory matrices as input and incorporates Hausdorff distance to identify sub-matrices that statistically significantly discriminate between the two groups of labelled trajectory matrices. Utilizing 2015/16 SportVU NBA tracking data, agent trajectory matrices representing attacks consisting of the trajectories of five agents (the ball, shooter, last passer, shooter defender, and last passer defender), were truncated to correspond to the time interval following the receipt of the ball by the last passer, and labelled as effective or ineffective based on a definition of attack effectiveness that we devise in the current study. After identifying appropriate parameters for MA-Stat-DSM by iteratively applying it to all matches involving the two top- and two bottom-placed teams from the 2015/16 NBA season, the method was then applied to selected matches and could identify and visualize the portions of plays, e.g., involving passing, on-, and/or off-the-ball movements, which were most relevant in rendering attacks effective or ineffective.

DOI： 10.1515/jqas-2023-0039

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Other Link： https://www.degruyter.com/document/doi/10.1515/jqas-2023-0039/pdf

Mathematical models for off-ball scoring prediction in basketball Reviewed

Rikako Kono, Keisuke Fujii

11th Workshop on Machine Learning and Data Mining for Sports Analytics 2024 (MLSA'24) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD'24) 2024.9

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (international conference proceedings)

In professional basketball, the accurate prediction of scoring opportunities
based on strategic decision-making is crucial for spatial and player
evaluations. However, traditional models often face challenges in accounting
for the complexities of off-ball movements, which are essential for
comprehensive performance evaluations. In this study, we propose two
mathematical models to predict off-ball scoring opportunities in basketball,
considering pass-to-score and dribble-to-score sequences: the Ball Movement for
Off-ball Scoring (BMOS) and the Ball Intercept and Movement for Off-ball
Scoring (BIMOS) models. The BMOS model adapts principles from the Off-Ball
Scoring Opportunities (OBSO) model, originally designed for soccer, to
basketball, whereas the BIMOS model also incorporates the likelihood of
interception during ball movements. We evaluated these models using player
tracking data from 630 NBA games in the 2015-2016 regular season, demonstrating
that the BIMOS model outperforms the BMOS model in terms of team scoring
prediction accuracy, while also highlighting its potential for further
development. Overall, the BIMOS model provides valuable insights for tactical
analysis and player evaluation in basketball.

arXiv

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Other Link： http://arxiv.org/pdf/2406.08749v2

Force-Based Modeling of Heterogeneous Roles in the Coordinated Behavior of a Triad Reviewed

Jun Ichikawa, Keisuke Fujii

New Generation Computing Vol. 42 ( 5 ) page： 983 - 995 2024.8

Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：Springer Science and Business Media LLC

Abstract

Group coordination is defined as interactions with other members to implement a task that is difficult to do alone or to achieve higher performance than an individual. Meanwhile, the adjustment process in coordination is not uniquely determined because each individual has many degrees of freedom. It is more difficult to explain and model complex and dynamic coordination, such as nonverbal behavior of three or more members than pair or verbal interaction. Hence, we previously introduced a coordinated drawing task and conducted the behavioral experiment. The triads operated reels to change the tensions of threads connected to a pen, shared three heterogeneous roles (pulling, relaxing, and adjusting), and moved the pen to draw an equilateral triangle. The results indicated that the adjusting role was related to high task performance by helping resiliently without disturbing the pen’s smooth movement while avoiding great pen deviation. However, this experiment alone cannot explain details of the adjustment process of tension. To supplement these findings, this study formulated the three roles using equations of motion. The multi-agent simulation results showed that the adjusting role might use the degree of pen deviation reflected by the others’ motor information, such as the operating procedures and forces, to change the tension and draw at least three sides. Although it is necessary to consider that we used the experimental task, our study contributes to the fundamental understanding of resilient adjustment in coordination by sharing heterogeneous roles as the first step.

DOI： 10.1007/s00354-024-00277-y

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Other Link： https://link.springer.com/article/10.1007/s00354-024-00277-y/fulltext.html

Open-Vocabulary Predictive World Models from Sensor Observations. Reviewed International journal Open Access

Robin Karlsson, Ruslan Asfandiyarov, Alexander Carballo, Keisuke Fujii, Kento Ohtani, Kazuya Takeda

Sensors Vol. 24 ( 14 ) page： 4735 - 4735 2024.7

Language：English Publishing type：Research paper (scientific journal)

Cognitive scientists believe that adaptable intelligent agents like humans perform spatial reasoning tasks by learned causal mental simulation. The problem of learning these simulations is called predictive world modeling. We present the first framework for a learning open-vocabulary predictive world model (OV-PWM) from sensor observations. The model is implemented through a hierarchical variational autoencoder (HVAE) capable of predicting diverse and accurate fully observed environments from accumulated partial observations. We show that the OV-PWM can model high-dimensional embedding maps of latent compositional embeddings representing sets of overlapping semantics inferable by sufficient similarity inference. The OV-PWM simplifies the prior two-stage closed-set PWM approach to the single-stage end-to-end learning method. CARLA simulator experiments show that the OV-PWM can learn compact latent representations and generate diverse and accurate worlds with fine details like road markings, achieving 69 mIoU over six query semantics on an urban evaluation sequence. We propose the OV-PWM as a versatile continual learning paradigm for providing spatio-semantic memory and learned internal simulation capabilities to future general-purpose mobile robots.

DOI： 10.3390/s24144735

Open Access

PubMed

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Unveiling Multi-Agent Strategies: A Data-Driven Approach for Extracting and Evaluating Team Tactics from Football Event and Freeze-Frame Data. Reviewed

Calvin Yeung 0001, Rory P. Bunker, Keisuke Fujii 0001

Journal of Robotics and Mechatronics Vol. 36 ( 3 ) page： 603 - 617 2024.6

Authorship：Last author,　Corresponding author Language：English Publishing type：Research paper (scientific journal) Publisher：Fuji Technology Press Ltd.

Studying collective behavior in opposing multi-agent teams is crucial across game theory, robotics, and sports analytics. In sports, especially football, team tactics involve intricate strategic spatial and action behaviors displayed as event sequences during possession. Understanding and analyzing these tactics is essential for successful training, strategic planning, and on-field success. While traditional approaches, such as notational and statistical analyses, offer valuable insights into team tactics, they often lack a comprehensive consideration of contextual information, thereby limiting the holistic evaluation of teams’ performances. To bridge this gap and capture the nuanced intricacies of team tactics, we employed advanced methodologies. The sequential pattern mining algorithm PrefixSpan was utilized to extract tactical patterns from possession sequences, enabling a deeper understanding of how teams strategize and adapt during play. Additionally, the neural marked spatio temporal point process (NMSTPP) model was leveraged to model and predict team behaviors, facilitating a fair comparison among teams. The evaluation of team possessions was further enhanced through the innovative holistic possession utilization score metrics, providing a more nuanced assessment of performance. In our experimental exploration, we identified and classified five distinct team tactics, validated the efficacy of the NMSTPP model when integrating StatsBomb 360 data, and conducted a comprehensive analysis of English Premier League teams during the 2022/2023 season. The results were visualized using radar plots and scatter plots with mean shift clustering. Lastly, the potential applications to RoboCup were discussed.

DOI： 10.20965/jrm.2024.p0603

CiNii Research

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Force-Based Modeling of a Resilient Helping Role in Coordinated Behavior of a Triad. Reviewed

Jun Ichikawa, Keisuke Fujii

26th International Conference on Human-Computer Interaction (HCI International 2024) Vol. 56 page： 148 - 155 2024.6

TeamTrack: A Dataset for Multi-Sport Multi-Object Tracking in Full-pitch Videos. Reviewed

Atom Sco, Ikuma Uchida, Ning Ding, Rikuhei Umemoto, Rory P. Bunker, Ren Kobayashi, Takeshi Koyama, Masaki Onishi, Yoshinari Kameda, Keisuke Fujii

IEEE/CVF Conference on Computer Vision and Pattern Recognition page： 3357 - 3366 2024.6

Pseudo-label based unsupervised fine-tuning of a monocular 3D pose estimation model for sports motions. Reviewed

Tomohiro Suzuki, Ryota Tanaka, Kazuya Takeda, Keisuke Fujii

IEEE/CVF Conference on Computer Vision and Pattern Recognition page： 3315 - 3324 2024.6

AutoSoccerPose: Automated 3D posture Analysis of Soccer Shot Movements. Reviewed

Calvin Yeung, Kenjiro Ide, Keisuke Fujii

IEEE/CVF Conference on Computer Vision and Pattern Recognition page： 3214 - 3224 2024.6

Collaborative hunting in artificial agents with deep reinforcement learning Reviewed

Kazushi Tsutsui, Ryoya Tanaka, Kazuya Takeda, Keisuke Fujii

eLife 2024.5

Authorship：Last author Language：English Publishing type：Research paper (scientific journal) Publisher：Cold Spring Harbor Laboratory

ABSTRACT

Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed as an advanced hunting strategy requiring large brains that involve high level cognition. However, recent findings that collaborative hunting have also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes using computational multi-agent simulation based on deep reinforcement learning. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between observations and actions via distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our results of computational ecology emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.

DOI： 10.1101/2022.10.10.511517

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Estimation of control area in badminton doubles with pose information from top and back view drone videos. Reviewed Open Access

Ning Ding, Kazuya Takeda, Wenhui Jin, Yingjiu Bei, Keisuke Fujii 0001

Multimedia Tools and Applications Vol. 83 ( 8 ) page： 24777 - 24793 2024.5

Language：English Publishing type：Research paper (scientific journal) Publisher：Springer Science and Business Media LLC

Abstract

The application of visual tracking to the performance analysis of sports players in dynamic competitions is vital for effective coaching. In doubles matches, coordinated positioning is crucial for maintaining control of the court and minimizing opponents’ scoring opportunities. The analysis of such teamwork plays a vital role in understanding the dynamics of the game. However, previous studies have primarily focused on analyzing and assessing singles players without considering occlusion in broadcast videos. These studies have relied on discrete representations, which involve the analysis and representation of specific actions (e.g., strokes) or events that occur during the game while overlooking the meaningful spatial distribution. In this work, we present the first annotated drone dataset from top and back views in badminton doubles and propose a framework to estimate the control area probability map, which can be used to evaluate teamwork performance. We present an efficient framework of deep neural networks that enables the calculation of full probability surfaces. This framework utilizes the embedding of a Gaussian mixture map of players’ positions and employs graph convolution on their poses. In the experiment, we verify our approach by comparing various baselines and discovering the correlations between the score and control area. Additionally, we propose a practical application for assessing optimal positioning to provide instructions during a game. Our approach offers both visual and quantitative evaluations of players’ movements, thereby providing valuable insights into doubles teamwork. The dataset and related project code is available at https://github.com/Ning-D/Drone_BD_ControlArea

DOI： 10.1007/s11042-023-16362-1

Open Access

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Other Link： https://link.springer.com/article/10.1007/s11042-023-16362-1/fulltext.html

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Papers - FUJII Keisuke