Vehicle color classification using manifold learning methods from urban surveillance videos

Yu Chen Wang, Chin Chuan Han, Chen Ta Hsieh, Kuo Chin Fan

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


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.

Original languageEnglish
Article number48
JournalEurasip Journal on Image and Video Processing
Issue number1
StatePublished - 2014


  • Coarse-to-fine strategy
  • Color classification
  • Dimension reduction
  • Manifold learning
  • Nearest feature line


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