Discriminative human action recognition in the learned hierarchical manifold space

Authors:
Lei Han;Xinxiao Wu;Wei Liang;Guangming Hou;Yunde Jia
Affiliations:
Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing 100081, PR China;Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing 100081, PR China;Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing 100081, PR China;Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing 100081, PR China;Beijing Laboratory of Intelligent Information Technology, Beijing Institute of Technology, Beijing 100081, PR China
Venue:
Image and Vision Computing
Year:
2010

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Abstract

In this paper, we propose a hierarchical discriminative approach for human action recognition. It consists of feature extraction with mutual motion pattern analysis and discriminative action modeling in the hierarchical manifold space. Hierarchical Gaussian Process Latent Variable Model (HGPLVM) is employed to learn the hierarchical manifold space in which motion patterns are extracted. A cascade CRF is also presented to estimate the motion patterns in the corresponding manifold subspace, and the trained SVM classifier predicts the action label for the current observation. Using motion capture data, we test our method and evaluate how body parts make effect on human action recognition. The results on our test set of synthetic images are also presented to demonstrate the robustness.