Obstacle Detection System Based On SURF and Saliency-map
碩士 === 國立宜蘭大學 === 電機工程學系碩士班 === 103 === In this paper, we propose an obstacle detection system using SURF method, SVM model and saliency map. In addition, we present fuzzy weighting to adjust saliency map by histogram-based contract (HC) method. The proposed detection system composes of three major...
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ndltd-TW-103NIU004420072019-05-15T22:07:29Z http://ndltd.ncl.edu.tw/handle/7g427d Obstacle Detection System Based On SURF and Saliency-map 基於SURF與顯著影像之障礙物偵測系統 Huang, Ya-Han 黃雅涵 碩士 國立宜蘭大學 電機工程學系碩士班 103 In this paper, we propose an obstacle detection system using SURF method, SVM model and saliency map. In addition, we present fuzzy weighting to adjust saliency map by histogram-based contract (HC) method. The proposed detection system composes of three major steps: First, the dense optical flow is used to extract the feature vectors of each pixel in the image sequence. The feature vectors are the training data of SVM. Second, the local feature points are detected by SURF method and are classified into the obstacle points and others by the trained SVM model in test stage. Finally, the obstacle points are combined with saliency map based on fuzzy weighting to find those with higher salient values. And then, these points are used to estimate the region of the obstacle. This method is a vision-based obstacle detection with single camera. It’s different from image subtraction method to detect the obstacle. Experimental results show that the system can accurately locate the obstacle position in the indoor and outdoor environments. Tao,Chin-Wang 陶金旺 2015 學位論文 ; thesis 51 zh-TW |
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碩士 === 國立宜蘭大學 === 電機工程學系碩士班 === 103 === In this paper, we propose an obstacle detection system using SURF method, SVM model and saliency map. In addition, we present fuzzy weighting to adjust saliency map by histogram-based contract (HC) method. The proposed detection system composes of three major steps: First, the dense optical flow is used to extract the feature vectors of each pixel in the image sequence. The feature vectors are the training data of SVM. Second, the local feature points are detected by SURF method and are classified into the obstacle points and others by the trained SVM model in test stage. Finally, the obstacle points are combined with saliency map based on fuzzy weighting to find those with higher salient values. And then, these points are used to estimate the region of the obstacle. This method is a vision-based obstacle detection with single camera. It’s different from image subtraction method to detect the obstacle. Experimental results show that the system can accurately locate the obstacle position in the indoor and outdoor environments.
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Tao,Chin-Wang |
author_facet |
Tao,Chin-Wang Huang, Ya-Han 黃雅涵 |
author |
Huang, Ya-Han 黃雅涵 |
spellingShingle |
Huang, Ya-Han 黃雅涵 Obstacle Detection System Based On SURF and Saliency-map |
author_sort |
Huang, Ya-Han |
title |
Obstacle Detection System Based On SURF and Saliency-map |
title_short |
Obstacle Detection System Based On SURF and Saliency-map |
title_full |
Obstacle Detection System Based On SURF and Saliency-map |
title_fullStr |
Obstacle Detection System Based On SURF and Saliency-map |
title_full_unstemmed |
Obstacle Detection System Based On SURF and Saliency-map |
title_sort |
obstacle detection system based on surf and saliency-map |
publishDate |
2015 |
url |
http://ndltd.ncl.edu.tw/handle/7g427d |
work_keys_str_mv |
AT huangyahan obstacledetectionsystembasedonsurfandsaliencymap AT huángyǎhán obstacledetectionsystembasedonsurfandsaliencymap AT huangyahan jīyúsurfyǔxiǎnzheyǐngxiàngzhīzhàngàiwùzhēncèxìtǒng AT huángyǎhán jīyúsurfyǔxiǎnzheyǐngxiàngzhīzhàngàiwùzhēncèxìtǒng |
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