RGB-D action recognition based on discriminative common structure learning model

Tianshan Liu; Jun Kong; Min Jiang; Hongtao Huo

doi:10.1117/1.JEI.28.2.023012

13 March 2019 RGB-D action recognition based on discriminative common structure learning model

Tianshan Liu, Jun Kong, Min Jiang, Hongtao Huo

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 2, 023012 (March 2019). https://doi.org/10.1117/1.JEI.28.2.023012

Abstract

The emergence of low-cost depth cameras creates potential for RGB-D based human action recognition. However, most of the existing RGB-D based approaches simply concatenate original heterogeneous features without discovering the latent relations among different modalities. We propose a discriminative common structure learning (DCSL) model for human action recognition from RGB-D sequences. Specifically, we extract deep learning-based features and hand-crafted features from multimodal data (skeleton, depth, and RGB). In particular, we propose a deep architecture based on 3-D convolutional neural network to automatically extract deep spatiotemporal features from raw sequences. The proposed DCSL model utilizes a generalized version of collective matrix factorization to learn shared features among different modalities. To perform supervised learning and preserve intermodal similarity, we formulate a graph regularization term by considering both label information and similar geometric structure of multimodal data, which intends to improve the discriminative power of shared features. Moreover, we solve the objective function using an iterative optimization algorithm. Then, an improved collaborative representation classifier is employed to perform computationally efficient action recognition. Experimental results on four action datasets demonstrate the superior performance of the proposed method.

Citation Download Citation

Tianshan Liu, Jun Kong, Min Jiang, and Hongtao Huo "RGB-D action recognition based on discriminative common structure learning model," Journal of Electronic Imaging 28(2), 023012 (13 March 2019). https://doi.org/10.1117/1.JEI.28.2.023012

Received: 18 October 2018; Accepted: 26 February 2019; Published: 13 March 2019

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