29 citations · 38 across the 7 of their papers we have counts for
7 papers
Data-driven simulator of multi-animal behavior with unknown dynamics via offline and online reinforcement learning
Keisuke Fujii, Kazushi Tsutsui, Yu Teshima +6
Simulators of animal movements play a valuable role in studying behavior. Advances in imitation learning for robotics have expanded possibilities for reproducing human and animal m…
Runner re-identification from single-view running video in the open-world setting
Tomohiro Suzuki, Kazushi Tsutsui, Kazuya Takeda +1
In many sports, player re-identification is crucial for automatic video processing and analysis. However, most of the current studies on player re-identification in multi- or singl…
Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning
Hiroshi Nakahara, Kazushi Tsutsui, Kazuya Takeda +1
Analysis of invasive sports such as soccer is challenging because the game situation changes continuously in time and space, and multiple agents individually recognize the game sit…
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
Keisuke Fujii, Kazushi Tsutsui, Atom Scott +3
Modeling of real-world biological multi-agents is a fundamental problem in various scientific and engineering fields. Reinforcement learning (RL) is a powerful framework to generat…
Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities
Rikuhei Umemoto, Kazushi Tsutsui, Keisuke Fujii
Analyzing defenses in team sports is generally challenging because of the limited event data. Researchers have previously proposed methods to evaluate football team defense by pred…
Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction
Masakiyo Teranishi, Kazushi Tsutsui, Kazuya Takeda +1
Evaluating the individual movements for teammates in soccer players is crucial for assessing teamwork, scouting, and fan engagement. It has been said that players in a 90-min game…
Learning interaction rules from multi-animal trajectories via augmented behavioral models
Keisuke Fujii, Naoya Takeishi, Kazushi Tsutsui +10
Extracting the interaction rules of biological agents from movement sequences pose challenges in various domains. Granger causality is a practical framework for analyzing the inter…