Name
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. (C) 2019 SPIE and IS&T |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1117/1.JEI.28.2.023012 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
RGB-D action recognition,discriminative shared features,supervised collective matrix factorization,3-D convolutional neural network | Pattern recognition,Computer science,Structure learning,Action recognition,Artificial intelligence,RGB color model,Discriminative model | Journal |
Volume | Issue | ISSN |
28 | 2 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tianshan Liu | 1 | 9 | 4.27 |
| Jun Kong | 2 | 111 | 18.94 |
| Min Jiang | 3 | 39 | 13.65 |
| Hongtao Huo | 4 | 20 | 2.10 |