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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44043

Title: Real-time traffic light detection on resource-limited mobile platform
Authors: Yi-Tung Chiu
Duan-Yu Chen
Jun-Wei Hsieh
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: Real-time systems
Image color analysis
Feature extraction
Intelligent vehicles
Mobile communication
Image recognition
Date: 2014-05
Issue Date: 2017-11-13T06:22:34Z
Publisher: IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW)
Abstract: Abstract:Given the rapid expansion of car ownership worldwide, vehicle safety is an increasingly critical issue in the automobile industry. The reduced cost of intelligent mobile phones has made it economically feasible to develop intelligent systems for visual-based event detection for forward collision avoidance and mitigation. In this work, a real-time traffic red light recognition is proposed under mobile platforms. The proposed method consists of real-time traffic lights localization via image down-sampling, circular regions detection and further traffic lights recognition. Hough Transform is modified to fast localize the traffic light candidates. Finally, a strong classifier is made from multiple weak features is employed for further verifications. In the experiment, the detection rate can achieve above 70%. This shows that our proposed traffic light recognition can be applied in real world environments.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44043
Appears in Collections:[資訊工程學系] 演講及研討會

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