scholarly journals Action recognition by dense trajectories

CVPR 2011 ◽  
2011 ◽  
Author(s):  
Heng Wang ◽  
Alexander Klaser ◽  
Cordelia Schmid ◽  
Cheng-Lin Liu
Keyword(s):  
Action Recognition ◽  
Dense Trajectories

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

2020 ◽  
Vol 57 (24) ◽  
pp. 241003
Author(s):  
高德勇 Gao Deyong ◽  
康自兵 Kang Zibing ◽  
王松 Wang Song ◽  
王阳萍 Wang Yangping
Keyword(s):  
Action Recognition ◽  
Human Body ◽  
Dense Trajectories ◽  
Video Saliency

scholarly journals Human Action Recognition Using Improved Salient Dense Trajectories

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Qingwu Li ◽  
Haisu Cheng ◽  
Yan Zhou ◽  
Guanying Huo
Keyword(s):  
Action Recognition ◽  
State Of The Art ◽  
Human Action Recognition ◽  
Human Action ◽  
Interest Points ◽  
Dense Trajectories ◽  
Dense Trajectory ◽  
Sparse Coefficient ◽  
Active Research ◽  
Motion Saliency

Human action recognition in videos is a topic of active research in computer vision. Dense trajectory (DT) features were shown to be efficient for representing videos in state-of-the-art approaches. In this paper, we present a more effective approach of video representation using improved salient dense trajectories: first, detecting the motion salient region and extracting the dense trajectories by tracking interest points in each spatial scale separately and then refining the dense trajectories via the analysis of the motion saliency. Then, we compute several descriptors (i.e., trajectory displacement, HOG, HOF, and MBH) in the spatiotemporal volume aligned with the trajectories. Finally, in order to represent the videos better, we optimize the framework of bag-of-words according to the motion salient intensity distribution and the idea of sparse coefficient reconstruction. Our architecture is trained and evaluated on the four standard video actions datasets of KTH, UCF sports, HMDB51, and UCF50, and the experimental results show that our approach performs competitively comparing with the state-of-the-art results.

scholarly journals Dense Trajectories and Motion Boundary Descriptors for Action Recognition

2013 ◽  
Vol 103 (1) ◽  
pp. 60-79 ◽  
Author(s):  
Heng Wang ◽  
Alexander Kläser ◽  
Cordelia Schmid ◽  
Cheng-Lin Liu
Keyword(s):  
Action Recognition ◽  
Dense Trajectories ◽  
Motion Boundary
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