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Journal of Electrical and Computer Engineering
Volume 2017 (2017), Article ID 8191537, 6 pages
Research Article

Improved Collaborative Representation Classifier Based on -Regularized for Human Action Recognition

1City University of Hong Kong, Kowloon Tong, Hong Kong
2Beijing University of Posts and Telecommunications, Beijing, China
3State Key Laboratory of Coal Resources and Safe Mining, China University of Mining & Technology, Beijing, China
4University of Chinese Academy of Sciences, Beijing, China

Correspondence should be addressed to Ce Li

Received 10 April 2017; Revised 15 August 2017; Accepted 28 September 2017; Published 20 November 2017

Academic Editor: Naiyang Guan

Copyright © 2017 Shirui Huo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Human action recognition is an important recent challenging task. Projecting depth images onto three depth motion maps (DMMs) and extracting deep convolutional neural network (DCNN) features are discriminant descriptor features to characterize the spatiotemporal information of a specific action from a sequence of depth images. In this paper, a unified improved collaborative representation framework is proposed in which the probability that a test sample belongs to the collaborative subspace of all classes can be well defined and calculated. The improved collaborative representation classifier (ICRC) based on -regularized for human action recognition is presented to maximize the likelihood that a test sample belongs to each class, then theoretical investigation into ICRC shows that it obtains a final classification by computing the likelihood for each class. Coupled with the DMMs and DCNN features, experiments on depth image-based action recognition, including MSRAction3D and MSRGesture3D datasets, demonstrate that the proposed approach successfully using a distance-based representation classifier achieves superior performance over the state-of-the-art methods, including SRC, CRC, and SVM.