Search Results for author:
Found 6 papers, 4 papers with code
Runner re-identification from single-view running video in the open-world setting
However, most of the current studies on player re-identification in multi- or single-view sports videos focus on re-identification in the closed-world setting using labeled image dataset, and player re-identification in the open-world setting for automatic video analysis is not well developed.
Action valuation of on- and off-ball soccer players based on multi-agent deep reinforcement learning
In this paper, we propose a method of valuing possible actions for on- and off-ball soccer players in a single holistic framework based on multi-agent deep reinforcement learning.
Adaptive action supervision in reinforcement learning from real-world multi-agent demonstrations
In the experiments, using chase-and-escape and football tasks with the different dynamics between the unknown source and target environments, we show that our approach achieved a balance between the reproducibility and the generalization ability compared with the baselines.
Location analysis of players in UEFA EURO 2020 and 2022 using generalized valuation of defense by estimating probabilities
Using the open-source location data of all players in broadcast video frames in football games of men's Euro 2020 and women's Euro 2022, we investigated the effect of the number of players on the prediction and validated our approach by analyzing the games.
Evaluation of creating scoring opportunities for teammates in soccer via trajectory prediction
However, it has remained difficult to evaluate an attacking player without receiving the ball, and to reveal how movement contributes to the creation of scoring opportunities for teammates.
Learning interaction rules from multi-animal trajectories via augmented behavioral models
In this paper, we propose a new framework for learning Granger causality from multi-animal trajectories via augmented theory-based behavioral models with interpretable data-driven models.
