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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44225

Title: An Automatic Traffic Surveillance System for Vehicle Tracking and Classification
Authors: Shih-Hao Yu
Jun-Wei Hsieh
Yung-Sheng Chen
Wen-Fong Hu
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Date: 2003
Issue Date: 2017-11-16T02:21:25Z
Publisher: Scandinavian Conference on Image Analysis
Abstract: Abstract:This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods which classify vehicles into only cars and non-cars, the proposed method has a good capability to categorize cars into more specific classes with a new “linearity” feature. In addition, in order to reduce occlusions of vehicles, an automatic scheme of detecting lane dividing lines is proposed. With the found lane dividing lines, not only occlusions of vehicles can be reduced but also a normalization scheme can be developed for tackling the problems of feature size variations. Once all vehicle features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes even though shadows, occlusions, and other noise exist. The designed classifier can collect different evidences from the database and the verified vehicle itself to make better decisions and thus much enhance the robustness and accuracy of classification. Experimental results show that the proposed method is much robust and powerful than other traditional methods.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44225
Appears in Collections:[資訊工程學系] 演講及研討會

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