Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiobject Behavior Recognition by Event Driven Selective Attention Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Role-based teamwork activity recognition in observations of embodied agent actions
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
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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A new approach of modeling/recognizing multi-agent activities from image sequences is presented. In recent years, Hidden Markov Models (HMMs) have been widely used to recognize activity units ranging from individual gestures to multi-people interactions. However, traditional HMMs meet many problems when the number of agents increases in the scene. One significant reason for this inability is the fact that HMMs require their 'observations' to be of fixed length and order. Unlike conventional HMMs, a new algorithm to model multi-agent activities is proposed. This has two sub-processes: one for modelling the activity based on decomposed observations and the other for recording the 'role' information of each agent in the activity. This new algorithm allows changing of the observations' length, and does not require initial agent assignment. The experimental results show that this algorithm is also robust when the agents' information is only partially represented.