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IEEE Transactions on Pattern Analysis and Machine Intelligence
An HMM-Based Threshold Model Approach for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
The Representation and Recognition of Human Movement Using Temporal Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Nonlinear PHMMs for the Interpretation of Parameterized Gesture
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
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FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Constructing Finite State Machines for Fast Gesture Recognition
ICPR '00 Proceedings of the International Conference on Pattern Recognition - Volume 3
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Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Real-Time Recognition of Human Gestures for 3D Interaction
AMDO '08 Proceedings of the 5th international conference on Articulated Motion and Deformable Objects
Toward natural interaction through visual recognition of body gestures in real-time
Interacting with Computers
Human action recognition using boosted EigenActions
Image and Vision Computing
Cyclic and non-cyclic gesture spotting and classification in real-time applications
AMDO'10 Proceedings of the 6th international conference on Articulated motion and deformable objects
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
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Computer Vision and Image Understanding
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In this paper, Primitive-based Dynamic Bayesian Networks are proposed for subject-independent natural action recognition. Inferred by high-level knowledge, Primitives are distinctive features that describe the context information and the motion information representing human action as well as pose. Dynamic Bayesian Networks could fuse multi-information so that many kinds of weak information could function as strong information for inference. The experimental results show that Primitive-based Dynamic Bayesian Networks not only increase the recognition rate but also improve the robustness.