An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
ACM Computing Surveys (CSUR)
Hierarchical Gaussian process latent variable models
Proceedings of the 24th international conference on Machine learning
Action-specific motion prior for efficient Bayesian 3D human body tracking
Pattern Recognition
Event analysis based on multiple interactive motion trajectories
IEEE Transactions on Circuits and Systems for Video Technology
International Journal of Computer Vision
Discriminative human action recognition in the learned hierarchical manifold space
Image and Vision Computing
Tracking human pose with multiple activity models
Pattern Recognition
2D action recognition serves 3D human pose estimation
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Monocular tracking of 3d human motion with a coordinated mixture of factor analyzers
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Real-Time Decentralized Articulated Motion Analysis and Object Tracking From Videos
IEEE Transactions on Image Processing
IEEE Transactions on Circuits and Systems for Video Technology
Machine Recognition of Human Activities: A Survey
IEEE Transactions on Circuits and Systems for Video Technology
Coupled Action Recognition and Pose Estimation from Multiple Views
International Journal of Computer Vision
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We present an approach to model articulated human movements and to analyse their behavioural semantics. First, we describe a novel dynamic and behavioural model that uses movements, a sequence of consecutive poses, from motion captured video data to establish priors for both tracking and behavioural analysis. Second, using that model, we show how we can both learn and subsequently recognise human activity. Activities are modelled and recognised independently to allow concurrent and complex actions. Finally, we combine activity recognition with tracking to produce an overall evaluation of the effectiveness of the approach using publicly available datasets.