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Learning Geometric Features with Dual–stream CNN for 3D Action Recognition
- Source :
- ICASSP
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Recently, regarding several beneficial properties of depth camera, numerous 3D action recognition frameworks have studied high-level features by exploiting deep learning techniques, but nevertheless they cannot seize the meaningful characteristics of static human pose and dynamic action motion of a whole sequence. This paper introduces a deep network configured by two parallel streams of convolutional stacks for fully learning the deep intra-frame joint associations and inter-frame joint correlations, wherein the structure of each stream is learned from Inception-v3. In experiments, besides the compatibility verification with various backbone networks, the proposed approach achieves the state-of-theart performance in battle with several deep learning-based methods on the updated NTU RGB+D 120 dataset.
- Subjects :
- Computer science
business.industry
Deep learning
0202 electrical engineering, electronic engineering, information engineering
RGB color model
Action recognition
020206 networking & telecommunications
020201 artificial intelligence & image processing
Pattern recognition
02 engineering and technology
Artificial intelligence
business
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Accession number :
- edsair.doi...........3e36778701814462da8215cbd081c137