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  5. A Real-Time distance prediction via deep learning and microsoft kinect
 
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A Real-Time distance prediction via deep learning and microsoft kinect

Journal
IOP Conference Series: Earth and Environmental Science
ISSN
1755-1307
1755-1315
Date Issued
2022
Author(s)
Hwee Sheng Tham
Universiti Malaysia Perlis
Razaidi Hussin
Universiti Malaysia Perlis
Rizalafande Che Ismail
Universiti Malaysia Perlis
DOI
10.1088/1755-1315/1064/1/012048
Abstract
3D(Dimension) understanding has become the herald of computer vision and graphics research in the era of technology. It benefits many applications such as autonomous cars, robotics, and medical image processing. The pros and cons of 3D detection bring convenience to the human community instead of 2D detection. The 3D detection consists of RGB (Red, Green and Blue) colour images and depth images which are able to perform better than 2D in real. The current technology is relying on the high costing light detection and ranging (LiDAR). However, the use of Microsoft Kinect has replaced the LiDAR systems for 3D detection gradually. In this project, a Kinect camera is used to extract the depth of image information. From the depth images, the distance can be defined easily. As in the colour scale, the red colour is the nearest and the blue colour is the farthest. The depth image will turn black when reaching the limitation of the Kinect camera measuring range. The depth information collected will be trained with deep learning architecture to perform a real-time distance prediction.
File(s)
research repository notification.pdf (4.4 MB)
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