Vehicle color classification using manifold learning methods from urban surveillance videos

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/107490

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107490

Title:	Vehicle color classification using manifold learning methods from urban surveillance videos
Authors:	范國清;Wang, Yu-Chen;Han, Chin-Chuan;Hsieh, Chen-Ta;Fan, Kuo-Chin
Contributors:	資訊電機學院資訊工程學系
Keywords:	Biometrics;Classification;Classifiers;Color;Engineering;Filtering;Histograms;Image Processing and Computer Vision;Pattern Recognition;Signal,Image and Speech Processing;Vehicles;Video
Date:	2014-01-17
Issue Date:	2026-04-23 14:14:33 (UTC+8)
Publisher:	Cham: Springer International Publishing
Abstract:	摘要： Color identification of vehicles plays a significant role in crime detection. In this study, a novel scheme for the color identification of vehicles is proposed using the locating algorithm of regions of interest (ROIs) as well as the color histogram features from still images. A coarse-to-fine strategy was adopted to efficiently locate the ROIs for various vehicle types. Red patch labeling, geometrical-rule filtering, and a texture-based classifier were cascaded to locate the valid ROIs. A color space fusion together with a dimension reduction scheme was designed for color classification. Color histograms in ROIs were extracted and classified by a trained classifier. Seven different classes of color were identified in this work. Experiments were conducted to show the performance of the proposed method. The average rates of ROI location and color classification were 98.45% and 88.18%, respectively. Moreover, the classification efficiency of the proposed method was up to 18 frames per second. 其他題名： J Image Video Proc 出版者： Cham: Springer International Publishing 出版日期： 2014-10-16 出處： EURASIP journal on image and video processing, 2014-10, Vol.2014 (1), p.1-20, Article 48 資源來源： ProQuest Open Access Content Collection 版權： Wang et al.; licensee Springer. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 識別號： ISSN: 1687-5281 識別號： ISSN: 1687-5176 識別號： EISSN: 1687-5281 識別號： DOI: 10.1186/1687-5281-2014-48
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

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