Human-Body Action Recognition Based on Dense Trajectories and Video Saliency

Deyong Gao; Zibing Kang; Song Wang; Yangping Wang

doi:10.3788/LOP57.241003

Journals >Laser & Optoelectronics Progress >Volume 57 >Issue 24 >Page 241003 > Article

Laser & Optoelectronics Progress
Vol. 57, Issue 24, 241003 (2020)

Human-Body Action Recognition Based on Dense Trajectories and Video Saliency

Deyong Gao^1、2, Zibing Kang^1、*, Song Wang^1、2, and Yangping Wang^1、3

Author Affiliations

¹School of Electronic & Information Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, China;

²Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphic & Image Processing, Lanzhou, Gansu 730070, China;

³Gansu Provincial Key Laboratory of System Dynamics and Reliability of Rail Transport Equipment, Lanzhou, Gansu 730070, China

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DOI: 10.3788/LOP57.241003 Cite this Article Set citation alerts

Deyong Gao, Zibing Kang, Song Wang, Yangping Wang. Human-Body Action Recognition Based on Dense Trajectories and Video Saliency[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241003 Copy Citation Text

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Deyong Gao, Zibing Kang, Song Wang, Yangping Wang. Human-Body Action Recognition Based on Dense Trajectories and Video Saliency[J]. Laser & Optoelectronics Progress, 2020, 57(24): 241003

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