Camera-based observation of football games for analyzing multi-agent activities

Beetz, Michael; Hoyningen-Huene, Nico von; Bandouch, Jan; Kirchlechner, Bernhard; Gedikli, Suat; Maldonado, Alexis

doi:10.1145/1160633.1160638

Cited by 38 publications

(25 citation statements)

References 11 publications

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“…The role-based approach to ad hoc teamwork by Genter et al [18] determines role selection on the team's utility. In robot soccer, roles have been based on absolute position in the field [32], position relative to the ball [2,3,31] and robot trajectories, sometimes also considering positions of other players [48].Other work on team coordination through roles and dynamic positioning can be found for example in [28,27,13]. Desheng et al achieve collaboration between simulated agents via roles in situation calculus.…”

Section: Related Researchmentioning

confidence: 99%

“…However, coordination without negotiation [22] is more appropriate if an ad hoc approach is required. Tracking multiple soccer players' trajectories can for example be done using the camera feed, in combination with an analyzing system that segments motions into classified ball actions [2]. Extracting tactic events from human soccer video feeds has also been done using spatio-temporal interaction among players and the ball [48].…”

Section: Related Researchmentioning

confidence: 99%

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A Formalization of the Coach Problem

Schropp

Meyer

Ramamoorthy

2015

RoboCup 2014: Robot World Cup XVIII

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Abstract. Coordination is an important aspect of multi-agent teamwork. In the context of robot soccer in the RoboCup Standard Platform League, our focus is on the coach as an external observer of the team, aiming to provide his teammates with effective tactical advice during matches. The coach problem can be approached from different angles: in order to adapt the behaviour of his teammates, he should at first be able to perform plan recognition on their observable actions. Furthermore, in providing them with appropriate advice, he should still adhere to the norms and regulations of the match to prevent penalties for his team. Also, when teammates' profiles and attributes are unknown or the system is only partially observable, coordination should be more 'ad hoc' to ensure robustness of the Multi-Agent System (MAS). In this work, we present a formalization of the problem of designing a coach in robot soccer, employing a temporal deontic logical framework. The framework is based on agent organizations [14], in which social coordination and norms play an important part.

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Section: Related Researchmentioning

confidence: 99%

Section: Related Researchmentioning

confidence: 99%

A Formalization of the Coach Problem

Schropp

Meyer

Ramamoorthy

2015

RoboCup 2014: Robot World Cup XVIII

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“…Indeed there are already halfautomatic systems in the related area of TV soccer scene analysis, for example the ASPOGAMO system by Beetz et al (2006Beetz et al ( , 2007 proofing that soccer scene understanding in general is on the edge of being functional.…”

Section: State Of the Artmentioning

confidence: 99%

(A) VISION FOR 2050 - The Road Towards Image Understanding for a Human-Robot Soccer Match

Frese¹,

Laue²

2008

Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics Service

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Abstract:We believe it is possible to create the visual subsystem needed for the RoboCup 2050 challenge -a soccer match between humans and robots -within the next decade. In this position paper, we argue, that the basic techniques are available, but the main challenge will be to achieve the necessary robustness. We propose to address this challenge through the use of probabilistically modeled context, so for instance a visually indistinct circle is accepted as the ball, if it fits well with the ball's motion model and vice versa. Our vision is accompanied by a sequence of (partially already conducted) experiments for its verification. In these experiments, a human soccer player carries a helmet with a camera and an inertial sensor and the vision system has to extract all information from that data, a humanoid robot would need to take the human's place.

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“…Bhandari et al [3] applied unsupervised data mining and knowledge discovery techniques to recognize patterns in NBA basketball data. Recently, Beetz et al [2] developed a system for matching soccer ball motions to different action models using decision-trees. The work on behavior recognition in sports has focused primarily on the mapping of movement traces to low-level game actions (e.g., scoring and passing).…”

Section: Related Workmentioning

confidence: 99%

Policy recognition for multi-player tactical scenarios

Sukthankar

Sycara

2007

Proceedings of the 6th International Joint Conference on Autonomous Agents and Multiagent Systems

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This paper addresses the problem of recognizing policies given logs of battle scenarios from multi-player games. The ability to identify individual and team policies from observations is important for a wide range of applications including automated commentary generation, game coaching, and opponent modeling. We define a policy as a preference model over possible actions based on the game state, and a team policy as a collection of individual policies along with an assignment of players to policies. This paper explores two promising approaches for policy recognition: (1) a modelbased system for combining evidence from observed events using Dempster-Shafer theory, and (2) a data-driven discriminative classifier using support vector machines (SVMs). We evaluate our techniques on logs of real and simulated games played using Open Gaming Foundation d20, the rule system used by many popular tabletop games, including Dungeons and Dragons.

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Camera-based observation of football games for analyzing multi-agent activities

Cited by 38 publications

References 11 publications

A Formalization of the Coach Problem

A Formalization of the Coach Problem

(A) VISION FOR 2050 - The Road Towards Image Understanding for a Human-Robot Soccer Match

Policy recognition for multi-player tactical scenarios

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