Real-Time American Sign Language Recognition Using Desk and Wearable Computer Based Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Large-Scale Event Detection Using Semi-Hidden Markov Models
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Hierarchical hidden Markov models with general state hierarchy
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Event processing under uncertainty
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Hierarchical multi-channel hidden semi Markov graphical models for activity recognition
Computer Vision and Image Understanding
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Many interesting human actions involve multiple interacting agents and also have typical durations. Further, there is an inherent hierarchical organization of these activities. In order to model these we introduce a new family of hidden Markov models (HMMs) that provide compositional state representations in both space and time and also a recursive hierarchical structure for inference at higher levels of abstraction. In particular, we focus on two possible 2-layer structures - the Hierarchical-Semi Parallel Hidden Markov Model (HSPaHMM) and the Hierarchical Parallel Hidden Semi-Markov Model (HPaHSMM). The lower layer of HSPaHMM consists of multiple HMMs for each agent while the top layer consists of a single HSMM. HPaHSMM on the other hand has multiple HSMMs at the lower layer and a Markov chain at the top layer. We present efficient learning and decoding algorithms for these models and then demonstrate them first on synthetic time series data and then in an application for sign language recognition.