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
Recognition of Visual Activities and Interactions by Stochastic Parsing
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
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
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
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Hidden Markov Models for Speech Recognition
Hidden Markov Models for Speech Recognition
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Video Sequence Interpretation for Visual Surveillance
VS '00 Proceedings of the Third IEEE International Workshop on Visual Surveillance (VS'2000)
Learning to Recognize Human Action Sequences
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Hierarchical group process representation in multi-agent activity recognition
Image Communication
Review: The use of pervasive sensing for behaviour profiling - a survey
Pervasive and Mobile Computing
Hierarchical control models for multimodal process modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rejection of non-meaningful activities for HMM-based activity recognition system
Image and Vision Computing
Decomposition in hidden Markov models for activity recognition
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Interacting activity recognition using hierarchical durational-state dynamic bayesian network
PCM'06 Proceedings of the 7th Pacific Rim conference on Advances in Multimedia Information Processing
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
International Journal of Computer Vision
Video content categorization using the double decomposition
Multimedia Tools and Applications
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To automatically recognize multi-agent activities is a highly challenging task due to the complexity of the interactions between agents. The difficulties in this task stem from two aspects: firstly, the feature vectors derived from input data are of large dimensionality and variable length. Secondly, an efficient mapping of agents from input data to pre-defined activity models, known as agent assignment, is required. This paper presents a new method to model and classify multi-agent activities based on the proposed observation decomposed hidden Markov models (ODHMMs). To handle the feature vectors, we decomposed each original feature vector into a set of sub-feature vectors to keep the explored feature space consistent. Agent assignment is realized using a newly introduced parameter, which represents the 'role' of each agent. The experimental results show that the proposed method can successfully classify three-person activities with high accuracy and is less sensitive to incomplete data input.