Application of PSO Algorithm in Real-Time Lane Tracking
碩士 === 義守大學 === 電機工程學系碩士班 === 94 === In this paper, we propose a new method for land detection and tracking. We use the Particle Swarm Optimization (PSO) which is high adaptive to detect and track the lane position of the source image which is gotten in real time. Firstly, this method convert the co...
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ndltd-TW-094ISU054420252015-10-13T14:49:54Z http://ndltd.ncl.edu.tw/handle/14674034776587485228 Application of PSO Algorithm in Real-Time Lane Tracking 應用PSO演算法在即時車道追蹤 Zhi-Xu DAI 戴志旭 碩士 義守大學 電機工程學系碩士班 94 In this paper, we propose a new method for land detection and tracking. We use the Particle Swarm Optimization (PSO) which is high adaptive to detect and track the lane position of the source image which is gotten in real time. Firstly, this method convert the color image to the gray image which has rich gradation. Secondly, we aim on the region of interest to use a spatial filter for extract the lane feature, and then the vanishing point is used to build a fitness function. Finally, the fitness function is used by the Particle Swarm Optimization for searching correct the lane position. We built an image database which is actual and outdoor lane condition that include with lane mark, without lane mark, shadowy, cloudy, rainy, curvy, rough, etc. In experiments, 8,000 images were processed to evaluate the system performance. The experiment results show that the performance and the efficiency of the proposed method achieve the objection for real time lane detection and the tracing. Ching-Han CHEN 陳慶瀚 2006 學位論文 ; thesis 50 zh-TW |
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碩士 === 義守大學 === 電機工程學系碩士班 === 94 === In this paper, we propose a new method for land detection and tracking. We use the Particle Swarm Optimization (PSO) which is high adaptive to detect and track the lane position of the source image which is gotten in real time. Firstly, this method convert the color image to the gray image which has rich gradation. Secondly, we aim on the region of interest to use a spatial filter for extract the lane feature, and then the vanishing point is used to build a fitness function. Finally, the fitness function is used by the Particle Swarm Optimization for searching correct the lane position.
We built an image database which is actual and outdoor lane condition that include with lane mark, without lane mark, shadowy, cloudy, rainy, curvy, rough, etc. In experiments, 8,000 images were processed to evaluate the system performance. The experiment results show that the performance and the efficiency of the proposed method achieve the objection for real time lane detection and the tracing.
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Ching-Han CHEN |
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Ching-Han CHEN Zhi-Xu DAI 戴志旭 |
author |
Zhi-Xu DAI 戴志旭 |
spellingShingle |
Zhi-Xu DAI 戴志旭 Application of PSO Algorithm in Real-Time Lane Tracking |
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Zhi-Xu DAI |
title |
Application of PSO Algorithm in Real-Time Lane Tracking |
title_short |
Application of PSO Algorithm in Real-Time Lane Tracking |
title_full |
Application of PSO Algorithm in Real-Time Lane Tracking |
title_fullStr |
Application of PSO Algorithm in Real-Time Lane Tracking |
title_full_unstemmed |
Application of PSO Algorithm in Real-Time Lane Tracking |
title_sort |
application of pso algorithm in real-time lane tracking |
publishDate |
2006 |
url |
http://ndltd.ncl.edu.tw/handle/14674034776587485228 |
work_keys_str_mv |
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