The Hierarchical Hidden Markov Model: Analysis and Applications
Machine Learning
Key concepts in model selection: performance and generalizability
Journal of Mathematical Psychology
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
Recognizing planned multiperson action
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)
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
Large-Scale Event Detection Using Semi-Hidden Markov Models
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
Video-based event recognition: activity representation and probabilistic recognition methods
Computer Vision and Image Understanding - Special issue on event detection in video
Layered representations for learning and inferring office activity from multiple sensory channels
Computer Vision and Image Understanding - Special issue on event detection in video
Automatic Analysis of Multimodal Group Actions in Meetings
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Multiple agent event detection and representation in videos
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
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
Decomposition in hidden Markov models for activity recognition
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Multi channel sequence processing
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
Modeling individual and group actions in meetings with layered HMMs
IEEE Transactions on Multimedia
Hierarchical control models for multimodal process modeling
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we develop a novel multi-agent activity recognition method, which emphasizes the influences of group on individuals and the hierarchical dynamics of group activities. We believe that the dynamics of group process plays a dominative role in characterizing multi-agent activities, and interactive information between agents is embedded in the influences of the group on individuals. Within the dynamic probabilistic network framework, we present a hierarchical control model (HCM) which consists of three parts: multi-channel layer, control layer and abstract layers. The multi-channel layer models the individual dynamics, at which agent channels are independent conditioning on the control layer. HCM extracts the group process using the control layer and represents its hierarchical characteristics with multi-level abstract layers. We combine multiple feature streams coming from individuals and recover the complex structure of the activity in one compact model. In experiments, the performance of HCM is evaluated and is compared with some other models. The results show that HCM is suitable for recognizing long complex multi-agent activities.