Faculty Profiles - FUJII Keisuke

Papers - FUJII Keisuke

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Predictive World Models from Real-World Partial Observations. Reviewed

Robin Karlsson, Alexander Carballo, Keisuke Fujii 0001, Kento Ohtani, Kazuya Takeda

MOST page： 152 - 166 2023

Multi-Agent Deep-Learning Based Comparative Analysis of Team Sport Trajectories. Reviewed Open Access

Zhang Ziyi, Rory P. Bunker, Kazuya Takeda, Keisuke Fujii 0001

IEEE Access Vol. 11 page： 43305 - 43315 2023

Learning to Predict Navigational Patterns from Partial Observations Reviewed Open Access

Robin Karlsson, Alexander Carballo, Francisco Lepe-Salazar, Keisuke Fujii, Kento Ohtani, Kazuya Takeda

IEEE Robotics and Automation Letters 2023

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

Human beings cooperatively navigate rule-constrained environments by adhering to mutually known navigational patterns, which may be represented as directional pathways or road lanes. Inferring these navigational patterns from incompletely observed environments is required for intelligent mobile robots operating in unmapped locations. However, algorithmically defining these navigational patterns is nontrivial. This paper presents the first self-supervised learning (SSL) method for learning to infer navigational patterns in real-world environments from partial observations only. We explain how geometric data augmentation, predictive world modeling, and an information-theoretic regularizer enable our model to predict an unbiased local directional soft lane probability (DSLP) field in the limit of infinite data. We demonstrate how to infer global navigational patterns by fitting a maximum likelihood graph to the DSLP field. Experiments show that our SSL model outperforms two SOTA supervised lane graph prediction models on the nuScenes dataset. We propose our SSL method as a scalable and interpretable continual learning paradigm for navigation by perception.

DOI： 10.1109/LRA.2023.3291924

Open Access

Scopus

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Score prediction using multiple object tracking for analyzing movements in 2-vs-2 Handball. Reviewed

Ren Kobayashi, Rikuhei Umemoto, Kazuya Takeda, Keisuke Fujii 0001

GCCE page： 946 - 947 2023

Automatic Edge Error Judgment in Figure Skating Using 3D Pose Estimation from Inertial Sensors. Reviewed

Ryota Tanaka, Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii 0001

GCCE page： 1099 - 1100 2023

Automatic Edge Error Judgment in Figure Skating Using 3D Pose Estimation from a Monocular Camera and IMUs. Reviewed Open Access

Ryota Tanaka, Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii 0001

MMSports@MM page： 41 - 48 2023

Action Valuation of On- and Off-Ball Soccer Players Based on Multi-Agent Deep Reinforcement Learning. Reviewed Open Access

Hiroshi Nakahara, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii 0001

IEEE Access Vol. 11 page： 131237 - 131244 2023

A sense of distance and movement characteristics of golfers tested without visual feedback of outcomes: Is a putt that feels subjectively good also physically good? Reviewed Open Access

Yumiko Hasegawa, Ayako Okada, Keisuke Fujii

Frontiers in Sports and Active Living Vol. 4 ( 987493 ) 2022.10

Automatic Fault Detection in Race Walking From a Smartphone Camera via Fine-Tuning Pose Estimation Reviewed

Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii

IEEE 11th Global Conference on Consumer Electronics (GCCE 2022) 2022.10

集団運動における機械学習を用いたデータ科学 Reviewed Open Access

藤井慶輔

人工知能学会研究会資料人工知能基本問題研究会 Vol. 121 ( 0 ) page： 18 - 18 2022.9

Optimization of aircraft flight paths considering the conflicting parameters of economy and safety Reviewed

Tetsuya Nagashima, Ming Ding, Keisuke Fujii, Kazuya Takeda

33rd Congress of the International Council of the Aeronautical Sciences 2022.9

Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction Reviewed

Masakiyo Teranishi, Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

9th Workshop on Machine Learning and Data Mining for Sports Analytics 2022 (MLSA'22) co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD'22) 2022.9

Emergence of Collaborative Hunting via Multi-Agent Deep Reinforcement Learning, International Workshop on Human Behavior Understanding Reviewed

Kazushi Tsutsui, Kazuya Takeda, Keisuke Fujii

2022.8

SoccerTrack: A Dataset and Tracking Algorithm for Soccer with Fish-eye and Drone Videos. Reviewed

Atom Scott, Ikuma Uchida, Masaki Onishi, Yoshinari Kameda, Kazuhiro Fukui, Keisuke Fujii 0001

CVPR Workshops page： 3568 - 3578 2022.6

Deep Reinforcement Learning in a Racket Sport for Player Evaluation With Technical and Tactical Contexts. Reviewed Open Access

Ning Ding, Kazuya Takeda, Keisuke Fujii

IEEE Access Vol. 10 page： 54764 - 54772 2022.5

Discriminant Dynamic Mode Decomposition for Labeled Spatiotemporal Data Collections. Reviewed Open Access

Naoya Takeishi, Keisuke Fujii, Koh Takeuchi, Yoshinobu Kawahara

SIAM J. Appl. Dyn. Syst. Vol. 21 ( 2 ) page： 1030 - 1058 2022.5

How does AI play football? An analysis of RL and real-world football strategies Reviewed

Atom Scott, Keisuke Fujii, Masaki Onishi

International Conference on Agents and Artificial Intelligence (ICAART 2022) Vol. 1 page： 42 - 52 2022.2

Evaluation of soccer team defense based on prediction models of ball recovery and being attacked: A pilot study Reviewed Open Access

Kosuke Toda, Masakiyo Teranishi, Keisuke Kushiro & Keisuke Fujii

PLoS One Vol. 17 ( 1 ) page： e0263051 2022.1

Estimating the Effect of Team Hitting Strategies Using Counterfactual Virtual Simulation in Baseball Reviewed Open Access

NAKAHARA Hiroshi, TAKEDA Kazuya, FUJII Keisuke

Proceedings of the Annual Conference of JSAI Vol. JSAI2022 ( 0 ) page： 3G4OS15b04 - 3G4OS15b04 2022

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Japanese Society for Artificial Intelligence

<p>In baseball, every play on the field is quantitatively evaluated and has an effect on individual and team strategies. The weighted on base average (wOBA) is well known as a measure of an batter's hitting contribution. However, this measure ignores the game situation, such as the runners on base, which coaches and batters are known to consider when employing multiple hitting strategies. Yet, the effectiveness of these strategies is unknown. This is probably because (1) we cannot obtain the batter's strategy and (2) it is difficult to estimate the effect of the strategies. Here, we propose a new method for estimating the effect using counterfactual batting simulation. To this end, we propose a deep learning model that transforms batting ability when batting strategy is changed. This method can estimate the effects of various strategies, which has been traditionally difficult with actual game data. We found that, when the switching cost of batting strategies can be ignored, the use of different strategies increased runs. When the switching cost is considered, the conditions for increasing runs are limited. Our validation results suggest that our simulation could clarify the effect of using multiple batting strategies.</p>

DOI： 10.11517/pjsai.jsai2022.0_3g4os15b04

Open Access

CiNii Research

Diversity of behavioral strategy in cooperative hunting using multi-agent deep reinforcement learning Reviewed Open Access

TSUTSUI Kazushi, TAKEDA Kazuya, FUJII Keisuke

Proceedings of the Annual Conference of JSAI Vol. JSAI2022 ( 0 ) page： 3G4OS15b03 - 3G4OS15b03 2022

Language：Japanese Publishing type：Research paper (scientific journal) Publisher：The Japanese Society for Artificial Intelligence

<p>Cooperative hunting is a widespread form of cooperation in nature, and it is known that the level of organization of this predation varies among species. However, how cooperative forms of predation have evolved and been maintained is not well understood. In this study, we addressed this issue using a multi-agent simulation based on deep reinforcement learning. We examined changes in behavioral strategies when changing factors that have been suggested to be associated with predation forms by previous observations in nature, and found that the highest level of organization with role division among individuals was emerged under the combined conditions of two factors: difficulty of prey capture, and food (reward) sharing. These results suggest that sophisticated predation forms, which have been thought to require high cognition, can evolve from relatively simple cognitive and learning mechanisms, and emphasize the close link between the predation form and the environment where the organism lives.</p>

DOI： 10.11517/pjsai.jsai2022.0_3g4os15b03

Open Access

CiNii Research

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Papers - FUJII Keisuke