The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
A Bayesian Computer Vision System for Modeling Human Interactions
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
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Recognition of Group Activities using Dynamic Probabilistic Networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Large-Scale Event Detection Using Semi-Hidden Markov Models
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Recognizing Interaction Activities using Dynamic Bayesian Network
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
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
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Multi-agent activity recognition using observation decomposedhidden Markov models
Image and Vision Computing
Modeling interleaved hidden processes
Proceedings of the 25th international conference on Machine learning
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
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Dynamic probabilistic networks have been widely used in activity recognition. However, few models are competent for long-term complex activities involving multi-person interactions. Based on the study of activity characteristics, this paper proposes a decomposed hidden Markov model (DHMM) to capture the structures of activity both in time and space. The model combines spatial decomposition and hierarchical abstraction to reduce the complexity of state space as well as the number of parameters greatly, with consequent computational benefits in efficiency and accuracy. Experiments on two-person interactions and individual activities demonstrate that DHMMs are more powerful than Coupled HMMs and Hierarchical HMMs.