The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Key concepts in model selection: performance and generalizability
Journal of Mathematical Psychology
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Recognition of Group Activities using Dynamic Probabilistic Networks
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
Hierarchical group process representation in multi-agent activity recognition
Image Communication
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 individual and group actions in meetings with layered HMMs
IEEE Transactions on Multimedia
Active and dynamic information fusion for multisensor systems with dynamic bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Group Interaction Analysis in Dynamic Context
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
The multimodal and hierarchical structure characteristics of a system make process modeling quite difficult. In this paper, we present a hierarchical control model (HCM) for hierarchically multimodal processing. From multiple streams, a control layer extracts the inherent group process that denotes the evolution of the system and controls the evolution of every modality. HCMs model the influences of the group on modalities and represent the hierarchical structure of the system by a multilayer network. To estimate the state order of the model, we also present a new information criterion that corrects the preference of traditional criteria for more complex models and proves the rationality of HCMs. Comparisons with other models on multiagent activity recognition show that HCMs are reliable and efficient.