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

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,volume 8333)

Abstract

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.

Keywords

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

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Rezaei, M., Terauchi, M. (2014). Vehicle Detection Based on Multi-feature Clues and Dempster-Shafer Fusion Theory. In: Klette, R., Rivera, M., Satoh, S. (eds) Image and Video Technology. PSIVT 2013. Lecture Notes in Computer Science, vol 8333. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53842-1_6

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  • DOI: https://doi.org/10.1007/978-3-642-53842-1_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-53841-4

  • Online ISBN: 978-3-642-53842-1

  • eBook Packages: Computer ScienceComputer Science (R0)