Pattern Recognition Letters
Semantic Representation and Recognition of Continued and Recursive Human Activities
International Journal of Computer Vision
Review: The use of pervasive sensing for behaviour profiling - a survey
Pervasive and Mobile Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
The use of hidden semi-Markov models in clinical diagnosis maze tasks
Intelligent Data Analysis
Activity recognition using semi-Markov models on real world smart home datasets
Journal of Ambient Intelligence and Smart Environments
Functional scene element recognition for video scene analysis
WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Human activity analysis: A review
ACM Computing Surveys (CSUR)
IWINAC'11 Proceedings of the 4th international conference on Interplay between natural and artificial computation - Volume Part I
Survey on classifying human actions through visual sensors
Artificial Intelligence Review
Visual code-sentences: a new video representation based on image descriptor sequences
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume Part I
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Video content categorization using the double decomposition
Multimedia Tools and Applications
Hierarchical multi-channel hidden semi Markov graphical models for activity recognition
Computer Vision and Image Understanding
Learning and parsing video events with goal and intent prediction
Computer Vision and Image Understanding
A novel evidence based model for detecting dangerous situations in level crossing environments
Expert Systems with Applications: An International Journal
Language-motivated approaches to action recognition
The Journal of Machine Learning Research
Keep it simple and sparse: real-time action recognition
The Journal of Machine Learning Research
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Recognizing human activity from a stream of sensory observations is important for a number of applications such as surveillance and human-computer interaction. Hidden Markov Models (HMMs) have been proposed as suitable tools for modeling the variations in the observations for the same action and for discriminating among different actions. HMMs have come in wide use for this task but the standard form suffers from several limitations. These include unrealistic models for the duration of a sub-event and not encoding interactions among multiple agents directly. Semi- Markov models and coupled HMMs have been proposed in previous work to handle these issues. We combine these two concepts into a coupled Hidden semi-Markov Model (CHSMM). CHSMMs pose huge computational complexity challenges. We present efficient algorithms for learning and decoding in such structures and demonstrate their utility by experiments with synthetic and real data.