CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
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
Computer
Interacting and Annealing Particle Filters: Mathematics and a Recipe for Applications
Journal of Mathematical Imaging and Vision
Constraint Integration for Efficient Multiview Pose Estimation with Self-Occlusions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Correction to "Gaussian Process Dynamical Models for Human Motion"
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human Pose Tracking in Monocular Sequence Using Multilevel Structured Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous Visual Recognition of Manipulation Actions and Manipulated Objects
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Inferring 3D body pose from silhouettes using activity manifold learning
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Subject-independent natural action recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Recognition and segmentation of 3-d human action using HMM and multi-class adaboost
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
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The recognition of human actions such as pointing at objects ("Give me that...") is difficult because they ought to be recognized independent of scene parameters such as viewing direction. Furthermore, the parameters of the action, such as pointing direction, are important pieces of information. One common way to achieve recognition is by using 3D human body tracking followed by action recognition based on the captured tracking data. General 3D body tracking is, however, still a difficult problem. In this paper, we are looking at human body tracking for action recognition from a context-driven perspective. Instead of the space of human body poses, we consider the space of possible actions of a given context and argue that 3D body tracking reduces to action tracking in the parameter space in which the actions live. This reduces the high-dimensional problem to a low-dimensional one. In our approach, we use parametric hidden Markov models to represent parametric movements; particle filtering is used to track in the space of action parameters. Our approach is content with monocular video data and we demonstrate its effectiveness on synthetic and on real image sequences. In the experiments we focus on human arm movements.