Learning Discriminative Key Poses for Action Recognition

Liu, Li, Shao, Ling, Zhen, Xiantong and Li, Xuelong (2013) Learning Discriminative Key Poses for Action Recognition. IEEE Transactions on Cybernetics, 43 (6). pp. 1860-1870. ISSN 2168-2267

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Abstract

In this paper, we present a new approach for human action recognition based on key-pose selection and representation. Poses in video frames are described by the proposed extensive pyramidal features (EPFs), which include the Gabor, Gaussian, and wavelet pyramids. These features are able to encode the orientation, intensity, and contour information and therefore provide an informative representation of human poses. Due to the fact that not all poses in a sequence are discriminative and representative, we further utilize the AdaBoost algorithm to learn a subset of discriminative poses. Given the boosted poses for each video sequence, a new classifier named weighted local naive Bayes nearest neighbor is proposed for the final action classification, which is demonstrated to be more accurate and robust than other classifiers, e.g., support vector machine (SVM) and naive Bayes nearest neighbor. The proposed method is systematically evaluated on the KTH data set, the Weizmann data set, the multiview IXMAS data set, and the challenging HMDB51 data set. Experimental results manifest that our method outperforms the state-of the-art techniques in terms of recognition rate.

Item Type:	Article
Uncontrolled Keywords:	AdaBoost, computer vision, extensive pyramidal features (EPFs), human action recognition, pose selection, weighted local naive Bayes nearest neighbor (WLNBNN) classifier
Subjects:	G400 Computer Science
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:	Publisher
Depositing User:	Paul Burns
Date Deposited:	10 Jun 2015 13:46
Last Modified:	12 Oct 2019 22:30
URI:	http://nrl.northumbria.ac.uk/id/eprint/22829

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