Deep Neural Networks Sensing for RGB-D Motion Recognition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 2121
Special Issue Editor
Interests: low-level vision; image classification; scene understanding; machine learning; computer vision; optimization
Special Issue Information
Dear Colleagues,
Human motion recognition is one of the essential branches of human-centered research activities. In recent years, motion recognition based on RGB-D data has attracted much attention. Along with the development of artificial intelligence, deep learning techniques have gained remarkable success in computer vision. In particular, convolutional neural networks (CNN) have achieved great success for image-based tasks, and recurrent neural networks (RNN) are renowned for sequence-based problems. Specifically, deep learning methods based on the CNN and RNN architectures have been adopted for motion recognition using RGB-D data.
In this special issue, we hope to discuss the latest progress in motion recognition based on RGB-D in detail and the future research direction.
Dr. Saeed Anwar
Guest Editor
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Keywords
- human-gait
- artificial vision
- RGB-D camera
- people tracking
- neurological disease
- fall prevention
- machine learning
- mobile robot
- RGB-D 3D reconstruction
- RGB-D human activity understanding
- RGB-D calibration
- RGB-D SLAM
- RGB-D data processing