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3D action recognition using multi-temporal skeleton visualization
- Source :
- ICME Workshops
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- Action recognition using depth sequences plays important role in many fields, e.g., intelligent surveillance, content-based video retrieval. Real applications require robust and accurate action recognition method. In this paper, we propose a skeleton visualization method, which efficiently encodes the spatial-temporal information of skeleton joints into a set of color images. These images are served as inputs for convolutional neural networks to extract more discriminative deep features. To enhance the ability of deep features to capture global relationships, we extend the color images into multi-temporal version. Additionally, to solve the effect of view point changes, a spatial transform method is adopted as a preprocessing step. Extensive experiments on NTU RGB+D dataset and ICME2017 challenge show that our method can accurately distinguish similar actions and shows robustness to view variations.
- Subjects :
- Computer science
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
02 engineering and technology
Convolutional neural network
Visualization
Discriminative model
Robustness (computer science)
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
RGB color model
Action recognition
020201 artificial intelligence & image processing
Computer vision
Artificial intelligence
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 IEEE International Conference on Multimedia & Expo Workshops (ICMEW)
- Accession number :
- edsair.doi...........14589e8dfbe089eb580185a9e5a29c9b