Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images
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 game...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-12-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/iet-ipr.2020.0148 |
| _version_ | 1818517536424067072 |
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| author | Ying Sun Yaoqing Weng Bowen Luo Gongfa Li Bo Tao Du Jiang Disi Chen |
| author_facet | Ying Sun Yaoqing Weng Bowen Luo Gongfa Li Bo Tao Du Jiang Disi Chen |
| author_sort | Ying Sun |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-12-11T00:57:33Z |
| format | Article |
| id | doaj.art-f67105f5bf894ba98f251d5cc336e0b4 |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-12-11T00:57:33Z |
| publishDate | 2020-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-f67105f5bf894ba98f251d5cc336e0b42022-12-22T01:26:26ZengWileyIET Image Processing1751-96591751-96672020-12-0114153662366810.1049/iet-ipr.2020.0148Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D imagesYing Sun0Yaoqing Weng1Bowen Luo2Gongfa Li3Bo Tao4Du Jiang5Disi Chen6Key Laboratory of Metallurgical Equipment and Control Technology Ministry of EducationWuhan University of Science and TechnologyWuhan430081People's Republic of ChinaResearch Center of Biologic Manipulator and Intelligent Measurement and ControlWuhan University of Science and TechnologyWuhan430081People's Republic of ChinaKey Laboratory of Metallurgical Equipment and Control Technology Ministry of EducationWuhan University of Science and TechnologyWuhan430081People's Republic of ChinaKey Laboratory of Metallurgical Equipment and Control Technology Ministry of EducationWuhan University of Science and TechnologyWuhan430081People's Republic of ChinaHubei Key Laboratory of Mechanical Transmission and Manufacturing EngineeringWuhan University of Science and TechnologyWuhan430081People's Republic of ChinaResearch Center of Biologic Manipulator and Intelligent Measurement and ControlWuhan University of Science and TechnologyWuhan430081People's Republic of ChinaSchool of ComputingUniversity of PortsmouthPortsmouthPO1 3HEUKWith 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%.https://doi.org/10.1049/iet-ipr.2020.0148gesture recognition algorithmmultiscale feature fusionRGB‐D imagessensor technologyartificial intelligencevideo gesture recognition technology |
| spellingShingle | Ying Sun Yaoqing Weng Bowen Luo Gongfa Li Bo Tao Du Jiang Disi Chen Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images IET Image Processing gesture recognition algorithm multiscale feature fusion RGB‐D images sensor technology artificial intelligence video gesture recognition technology |
| title | Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images |
| title_full | Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images |
| title_fullStr | Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images |
| title_full_unstemmed | Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images |
| title_short | Retracted: Gesture recognition algorithm based on multi‐scale feature fusion in RGB‐D images |
| title_sort | retracted gesture recognition algorithm based on multi scale feature fusion in rgb d images |
| topic | gesture recognition algorithm multiscale feature fusion RGB‐D images sensor technology artificial intelligence video gesture recognition technology |
| url | https://doi.org/10.1049/iet-ipr.2020.0148 |
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