Negative obstacle detection using stereo vision
Concepts of Binocular Stereo Vision come from our human vision system. When human eyes looking at certain objects, the reflection of light from the objects received by the eyes form images at the left and right retina. The images formed would be slightly different, there would be a shift in the hori...
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sg-ntu-dr.10356-677502023-07-07T17:03:10Z Negative obstacle detection using stereo vision You, Zhiyong Wang Han School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering Concepts of Binocular Stereo Vision come from our human vision system. When human eyes looking at certain objects, the reflection of light from the objects received by the eyes form images at the left and right retina. The images formed would be slightly different, there would be a shift in the horizontal axis known as disparity. Our brain would then compute the depth of the objects with the disparity, eventually, 3D scenes are constructed. Computer Binocular Vision make use of this theory, use two cameras to capture images from same scene, obtained the images with disparity. Then, according to the concept of Binocular Stereo Vision, a 3D space can be reconstructed. [1] Unmanned guided vehicles also known as intelligence vehicles are vehicles which can run autonomously and continuously on road. They are able to detect obstacles and avoid them accordingly without human intervention. In order to achieve such ability, they need to have perception on their surroundings. Computer Binocular Stereo Vision is one of the ways to help the machines to perceive their surroundings and make appropriate decisions upon it. A stereo vision system consist of following processes camera calibration, image rectification, stereo matching and triangulation. In this article, we would discuss the background and significant this the binocular stereo vision system, then the different steps of the systems would be further discuss and elaborate. Finally, we would discuss on the refinements that help to improve the systems. Bachelor of Engineering 2016-05-19T09:15:14Z 2016-05-19T09:15:14Z 2016 Final Year Project (FYP) http://hdl.handle.net/10356/67750 en Nanyang Technological University 39 p. application/pdf |
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DRNTU::Engineering::Electrical and electronic engineering You, Zhiyong Negative obstacle detection using stereo vision |
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Concepts of Binocular Stereo Vision come from our human vision system. When human eyes looking at certain objects, the reflection of light from the objects received by the eyes form images at the left and right retina. The images formed would be slightly different, there would be a shift in the horizontal axis known as disparity. Our brain would then compute the depth of the objects with the disparity, eventually, 3D scenes are constructed. Computer Binocular Vision make use of this theory, use two cameras to capture images from same scene, obtained the images with disparity. Then, according to the concept of Binocular Stereo Vision, a 3D space can be reconstructed. [1]
Unmanned guided vehicles also known as intelligence vehicles are vehicles which can run autonomously and continuously on road. They are able to detect obstacles and avoid them accordingly without human intervention. In order to achieve such ability, they need to have perception on their surroundings. Computer Binocular Stereo Vision is one of the ways to help the machines to perceive their surroundings and make appropriate decisions upon it.
A stereo vision system consist of following processes camera calibration, image rectification, stereo matching and triangulation. In this article, we would discuss the background and significant this the binocular stereo vision system, then the different steps of the systems would be further discuss and elaborate. Finally, we would discuss on the refinements that help to improve the systems. |
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Wang Han |
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Wang Han You, Zhiyong |
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Final Year Project |
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You, Zhiyong |
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You, Zhiyong |
title |
Negative obstacle detection using stereo vision |
title_short |
Negative obstacle detection using stereo vision |
title_full |
Negative obstacle detection using stereo vision |
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Negative obstacle detection using stereo vision |
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Negative obstacle detection using stereo vision |
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negative obstacle detection using stereo vision |
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2016 |
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http://hdl.handle.net/10356/67750 |
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1772829073836343296 |