A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
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
Vision-based human motion analysis: An overview
Computer Vision and Image Understanding
Human 3D Motion Recognition Based on Spatial-Temporal Context of Joints
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern 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
Hybrid generative-discriminative classification using posterior divergence
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We propose a novel human action recognition method based on the generative feature mapping over 3D human body joint sequences. The proposed method relies on Hidden Markov Model (HMM), but differs from the previous methods in the way of incorporating HMM and discriminative classifier, aiming to capture more discriminative information. Firstly, we use HMMs to model the joint sequences of human body. Then the Posterior Divergence is used to build feature mappings from the trained HMMs. The derived feature mappings map a variable-length joint sequence to a fixed-dimension feature vector which will be delivered to SVM for classification. We evaluate the proposed method and related methods on a large number of 3D joint sequences. The experimental results show its competitive performance, in comparison with other state-of-the-art methods.