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Scene image and human skeleton-based dual-stream human action recognition
- Source :
- Pattern Recognition Letters. 148:136-145
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- The dual stream-based human action recognition model offers the advantage of high recognition accuracy, but the algorithm is less robust in case of lighting changes. The human skeleton has a strong ability to express human behavior and actions; however, the scene information is ignored. Drawing on the idea of the dual-stream model, this paper proposes a human skeleton and scene image-based dual-stream model for human action recognition. The motion features are extracted through the spatio-temporal graph convolution of the human skeleton, and a scene recognition model is proposed based on the sparse frame sampling of video and video-level consensus strategy to process the scene video and gather the visual scene information. The proposed model exploits the advantages of skeleton information in motion expression and the superiority of the image in scene presentation. The scene information and spatio-temporal graph convolution-based human skeleton limbs are fused complementarily to achieve human action recognition. Compared to the conventional optical flow-based dual-stream action recognition method, this model is verified by experimenting under unstable light conditions, and the performance of human action recognition is robust and promising.
- Subjects :
- business.industry
Computer science
Frame (networking)
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Optical flow
DUAL (cognitive architecture)
Skeleton (category theory)
Expression (mathematics)
Convolution
Human skeleton
medicine.anatomical_structure
Artificial Intelligence
Signal Processing
medicine
Graph (abstract data type)
Computer vision
Computer Vision and Pattern Recognition
Artificial intelligence
business
Software
ComputingMethodologies_COMPUTERGRAPHICS
Subjects
Details
- ISSN :
- 01678655
- Volume :
- 148
- Database :
- OpenAIRE
- Journal :
- Pattern Recognition Letters
- Accession number :
- edsair.doi...........05ea182872c2045b5eb29e0831fe9048