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Vehicle detection using normalized color and edge map.

Luo-Wei Tsai1, Jun-Wei Hsieh, Kuo-Chin Fan

  • 1Department of Computer Engineering, National Central University, Chung-Li 320, Taiwan, R.O.C. echoo@fox1.csie.ncu.edu.tw

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|March 16, 2007
PubMed
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This study introduces a new vehicle detection method using color and edge features for static images. It achieves high accuracy by effectively identifying vehicle pixels under varied lighting conditions.

Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Traditional vehicle detection often relies on motion features, which are unsuitable for static images.
  • Detecting vehicles using color is challenging due to variations in lighting and weather conditions.

Purpose of the Study:

  • To develop a novel vehicle detection approach for static images.
  • To overcome limitations of existing methods by utilizing color and edge features.

Main Methods:

  • A new color transform model was developed to identify vehicle pixels, robust to illumination changes.
  • A cascade multichannel classifier was constructed using corner, edge map, and wavelet transform features.
  • A scanning process efficiently verifies candidate vehicles by pre-eliminating background pixels.

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Main Results:

  • The proposed method effectively detects vehicles from static images.
  • Integration of global color and local edge features proved powerful for vehicle detection.
  • An average accuracy rate of 94.9% was achieved in vehicle detection experiments.

Conclusions:

  • The novel color transform model enhances vehicle pixel identification under varying illumination.
  • The combined approach of color and edge features offers a robust solution for static vehicle detection.
  • This method provides a significant advancement in automated vehicle detection systems.