Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
A fault-tolerant multi-agent framework
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 2
LOTTO: group formation by overhearing in large teams
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Towards a Formal Approach to Overhearing: Algorithms for Conversation Identification
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Automatic annotation of team actions in observations of embodied agents
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Conditional random fields for activity recognition
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Simultaneous team assignment and behavior recognition from spatio-temporal agent traces
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Monitoring teams by overhearing: a multi-agent plan-recognition approach
Journal of Artificial Intelligence Research
Multi-agent activity recognition using observation decomposed hidden Markov model
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Discrete relative states to learn and recognize goals-based behaviors of groups
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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Recognizing team actions in the behavior of embodied agents has many practical applications and had seen significant progress in recent years. One approach with proven results is based on HMM-based recognition of spatio-temporal patterns in the behavior of the agents. While it had been shown to work on real-world datasets, this approach was found to be brittle. In this paper we present two contributions which together can significantly increase the robustness of teamwork activity recognition. First we introduce a technique to reduce high dimensional continuous input data to a set of discrete features, which capture the essential components of the team actions. Second, we prefix the actual team action recognition with a role recognition module, which allows us to present the recognizer with arbitrarily shuffled input, and still obtain high recognition rates. We validate the improved accuracy and robustness of the team action recognizer on datasets derived from captured real world data.