Vehicle Detection Based on Multi-feature Clues and Dempster-Shafer Fusion Theory

  • Mahdi Rezaei
  • Mutsuhiro Terauchi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8333)


On-road vehicle detection and rear-end crash prevention are demanding subjects in both academia and automotive industry. The paper focuses on monocular vision-based vehicle detection under challenging lighting conditions, being still an open topic in the area of driver assistance systems. The paper proposes an effective vehicle detection method based on multiple features analysis and Dempster-Shafer-based fusion theory. We also utilize a new idea of Adaptive Global Haar-like (AGHaar) features as a promising method for feature classification and vehicle detection in both daylight and night conditions. Validation tests and experimental results show superior detection results for day, night, rainy, and challenging conditions compared to state-of-the-art solutions.


Vehicle detection Monocular vision Collision detection Line and corner features Dempster-Shafer theory Data fusion 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Mahdi Rezaei
    • 1
  • Mutsuhiro Terauchi
    • 2
  1. 1.The University of AucklandNew Zealand
  2. 2.Hiroshima International UniversityJapan

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