NDLI: Gesture recognition algorithm based on multi-scale feature fusion in RGB-D images

Content Provider	IET Digital Library
Author	Sun, Ying Weng, Yaoqing Luo, Bowen Li, Gongfa Tao, Bo Jiang, Du Chen, Disi
Abstract	With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human–computer interaction more natural and flexible, bringing the richer interactive experience to teaching, on-board control, electronic games etc. To perform robust recognition under the conditions of illumination change, background clutter, rapid movement, and partial occlusion, an algorithm based on multi-level feature fusion of two-stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains red–green–blue-depth (RGB-D) images to establish a gesture database. At the same time, data enhancement is performed on the training set and test set. Then, a model of multi-level feature fusion of a two-stream convolutional neural network is established and trained. Experiments show that the proposed network model can robustly track and recognise gestures under complex backgrounds (such as similar complexion, illumination changes, and occlusion), and compared with the single-channel model, the average detection accuracy is improved by 1.08%, and mean average precision is improved by 3.56%.
Starting Page	3662
Ending Page	3668
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 15, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/15
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2020.0148
Journal	IET Image Processing
Publisher Date	2020-04-30
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Artificial Intelligence Background Clutter Big Data Complex Backgrounds Computer Vision And Image Processing Technique Convolutional Neural Nets Data Enhancement Electronic Game Gesture Database Gesture Recognition Gesture Recognition Algorithm Illumination Change Image Colour Analysis Image Recognition Image Sonsor Kinect Sensor Multilevel Feature Fusion Multiscale Feature Fusion Neural Computing Technique On-board Control Partial Occlusion Red-green-blue-depth Image RGB-D Image Robust Recognition Sensor Technology Two-stream Convolutional Neural Network Video Gesture Recognition Technology Video Signal Processing
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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