Knowledge Commons of Institute of Automation,CAS
Asymmetric 3D Convolutional Neural Networks for action recognition | |
Yang, Hao1,3; Yuan, Chunfeng1; Li, Bing1; Du, Yang1,3; Xing, Junliang1; Hu, Weiming1,2,3; Maybank, Stephen J.4 | |
发表期刊 | PATTERN RECOGNITION |
ISSN | 0031-3203 |
2019 | |
卷号 | 85期号:1页码:1-12 |
摘要 | Convolutional Neural Network based action recognition methods have achieved significant improvements in recent years. The 3D convolution extends the 2D convolution to the spatial-temporal domain for better analysis of human activities in videos. The 3D convolution, however, involves many more parameters than the 2D convolution. Thus, it is much more expensive on computation, costly on storage, and difficult to learn. This work proposes efficient asymmetric one-directional 3D convolutions to approximate the traditional 3D convolution. To improve the feature learning capacity of asymmetric 3D convolutions, a set of local 3D convolutional networks, called MicroNets, are proposed by incorporating multi-scale 3D convolution branches. Then, an asymmetric 3D-CNN deep model is constructed by MicroNets for the action recognition task. Moreover, to avoid training two networks on the RGB and Flow frames separately as most works do, a simple but effective multi-source enhanced input is proposed, which fuses useful information of the RGB and Flow frame at the pre-processing stage. The asymmetric 3D-CNN model is evaluated on two of the most challenging action recognition benchmarks, UCF-101 and HMDB-51. The asymmetric 3D-CNN model outperforms all the traditional 3D-CNN models in both effectiveness and efficiency, and its performance is comparable with that of recent state-of-the-art action recognition methods on both benchmarks. (C) 2018 Elsevier Ltd. All rights reserved. |
关键词 | Asymmetric 3D convolution MicroNets 3D-CNN Action recognition |
DOI | 10.1016/j.patcog.2018.07.028 |
关键词[WOS] | SHORT-TERM-MEMORY ; FEATURES ; VIDEOS ; TIME ; FLOW |
收录类别 | SCI |
语种 | 英语 |
WOS研究方向 | Computer Science ; Engineering |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
WOS记录号 | WOS:000447819300001 |
出版者 | ELSEVIER SCI LTD |
七大方向——子方向分类 | 图像视频处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/22816 |
专题 | 多模态人工智能系统全国重点实验室_视频内容安全 |
通讯作者 | Yuan, Chunfeng |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100190, Peoples R China 4.Birkbeck Coll, Dept Comp Sci & Informat Syst, London WC1E 7HX, England |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Yang, Hao,Yuan, Chunfeng,Li, Bing,et al. Asymmetric 3D Convolutional Neural Networks for action recognition[J]. PATTERN RECOGNITION,2019,85(1):1-12. |
APA | Yang, Hao.,Yuan, Chunfeng.,Li, Bing.,Du, Yang.,Xing, Junliang.,...&Maybank, Stephen J..(2019).Asymmetric 3D Convolutional Neural Networks for action recognition.PATTERN RECOGNITION,85(1),1-12. |
MLA | Yang, Hao,et al."Asymmetric 3D Convolutional Neural Networks for action recognition".PATTERN RECOGNITION 85.1(2019):1-12. |
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