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Department光电测控技术研究室(三室)
Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity
Guangle Yao1,2,3; Tao Lei1; Xianyuan Liu1,3; Ping Jiang1
Source PublicationAPPLIED SCIENCES-BASEL
Volume8Issue:10Pages:1924
2018-10-01
Language英语
ISSN2076-3417
DOI10.3390/app8101924
Indexed BySCI
WOS IDWOS:000448653700219
SubtypeJ
AbstractTemporal action detection in long, untrimmed videos is an important yet challenging task that requires not only recognizing the categories of actions in videos, but also localizing the start and end times of each action. Recent years, artificial neural networks, such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) improve the performance significantly in various computer vision tasks, including action detection. In this paper, we make the most of different granular classifiers and propose to detect action from fine to coarse granularity, which is also in line with the people's detection habits. Our action detection method is built in the proposal then classification' framework. We employ several neural network architectures as deep information extractor and segment-level (fine granular) and window-level (coarse granular) classifiers. Each of the proposal and classification steps is executed from the segment to window level. The experimental results show that our method not only achieves detection performance that is comparable to that of state-of-the-art methods, but also has a relatively balanced performance for different action categories.
Keywordaction detection action proposal convolutional neural network regression network
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ioe.ac.cn/handle/181551/9266
Collection光电测控技术研究室(三室)
Affiliation1.Institute of Optics and Electronics, Chinese Academy of Sciences, P.O. Box 350, Shuangliu, Chengdu 610209, China;
2.School of Optoelectronic Information, University of Electronic Science and Technology of China, No. 4, Section 2, North Jianshe Road, Chengdu 610054, China;
3.University of Chinese Academy of Sciences, 19 A Yuquan Rd, Shijingshan District, Beijing 100039, China
Recommended Citation
GB/T 7714
Guangle Yao,Tao Lei,Xianyuan Liu,et al. Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity[J]. APPLIED SCIENCES-BASEL,2018,8(10):1924.
APA Guangle Yao,Tao Lei,Xianyuan Liu,&Ping Jiang.(2018).Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity.APPLIED SCIENCES-BASEL,8(10),1924.
MLA Guangle Yao,et al."Temporal Action Detection in Untrimmed Videos from Fine to Coarse Granularity".APPLIED SCIENCES-BASEL 8.10(2018):1924.
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