Incorporating Omni-Directional Image and the Optical Flow Technique into Movement Estimation
碩士 === 國立中山大學 === 機械與機電工程學系研究所 === 95 === From the viewpoint of applications, conventional cameras are usually limited in their fields of view. The omni-directional camera has a full range in all directions, which gains the complete field of view. In the past, a moving object can be detected, only w...
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ndltd-TW-095NSYS54900552019-05-15T19:48:10Z http://ndltd.ncl.edu.tw/handle/pjjb7n Incorporating Omni-Directional Image and the Optical Flow Technique into Movement Estimation 應用全方位影像與光流技術於運動估測 Chia-Chih Chou 周家至 碩士 國立中山大學 機械與機電工程學系研究所 95 From the viewpoint of applications, conventional cameras are usually limited in their fields of view. The omni-directional camera has a full range in all directions, which gains the complete field of view. In the past, a moving object can be detected, only when the camera is static or moving with a known speed. If those methods are employed to mobile robots or vehicles, it will be difficult to determine the motion of moving objects observed by the camera. In this paper, we assume the omni-directional camera is mounted on a moving platform, which travels with a planar motion. The region of floor in the omni-directional image and the brightness constraint equation are applied to estimate the ego-motion. The depth information is acquired from the floor image to solve the problem that cannot be obtained by single camera systems. Using the estimated ego-motion, the optical flow caused by the floor motion can be computed. Therefore, comparing its direction with the direction of the optical flow on the image leads to detection of a moving object. Due to the depth information, even if the camera is in the condition that combining translational and rotational motions, a moving object can still be accurately identified. Chi-Cheng Cheng 程啟正 2007 學位論文 ; thesis 72 zh-TW |
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碩士 === 國立中山大學 === 機械與機電工程學系研究所 === 95 === From the viewpoint of applications, conventional cameras are usually limited in their fields of view. The omni-directional camera has a full range in all directions, which gains the complete field of view. In the past, a moving object can be detected, only when the camera is static or moving with a known speed. If those methods are employed to mobile robots or vehicles, it will be difficult to determine the motion of moving objects observed by the camera.
In this paper, we assume the omni-directional camera is mounted on a moving platform, which travels with a planar motion. The region of floor in the omni-directional image and the brightness constraint equation are applied to estimate the ego-motion. The depth information is acquired from the floor image to solve the problem that cannot be obtained by single camera systems. Using the estimated ego-motion, the optical flow caused by the floor motion can be computed. Therefore, comparing its direction with the direction of the optical flow on the image leads to detection of a moving object. Due to the depth information, even if the camera is in the condition that combining translational and rotational motions, a moving object can still be accurately identified.
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author2 |
Chi-Cheng Cheng |
author_facet |
Chi-Cheng Cheng Chia-Chih Chou 周家至 |
author |
Chia-Chih Chou 周家至 |
spellingShingle |
Chia-Chih Chou 周家至 Incorporating Omni-Directional Image and the Optical Flow Technique into Movement Estimation |
author_sort |
Chia-Chih Chou |
title |
Incorporating Omni-Directional Image and the Optical Flow Technique into Movement Estimation |
title_short |
Incorporating Omni-Directional Image and the Optical Flow Technique into Movement Estimation |
title_full |
Incorporating Omni-Directional Image and the Optical Flow Technique into Movement Estimation |
title_fullStr |
Incorporating Omni-Directional Image and the Optical Flow Technique into Movement Estimation |
title_full_unstemmed |
Incorporating Omni-Directional Image and the Optical Flow Technique into Movement Estimation |
title_sort |
incorporating omni-directional image and the optical flow technique into movement estimation |
publishDate |
2007 |
url |
http://ndltd.ncl.edu.tw/handle/pjjb7n |
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