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Vehicle Detection and Distance Estimation

  • Mahdi Rezaei
  • Reinhard Klette
Chapter
Part of the Computational Imaging and Vision book series (CIVI, volume 45)

Abstract

“Collision warning systems” are actively researched in the area of computer vision and the automotive industry. Using monocular vision only, this chapter discusses the part of our study that aims at detecting and tracking the vehicles ahead, to identify safety distances, and to provide timely information to assist a distracted driver under various weather and lighting conditions. As part of the work presented in this chapter, we also adopt the previously discussed dynamic global Haar (DGHaar) features for vehicle detection. We introduce “taillight segmentation” and a “virtual symmetry detection” technique for pairing the rear-light contours of the vehicles on the road. Applying a heuristic geometric solution, we also develop a method for inter-vehicle “distance estimation” using only a monocular vision sensor. Inspired by Dempster–Shafer theory, we finally fuse all the available clues and information to reach a higher degree of certainty. The proposed algorithm is able to detect vehicles ahead both at day and night, and also for a wide range of distances. Experimental results under various conditions, including sunny, rainy, foggy, or snowy weather, show that the proposed algorithm outperforms other currently published algorithms that are selected for comparison.

Keywords

False Alarm Distance Estimation Horizontal Edge Street Light Vehicle Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mahdi Rezaei
    • 1
  • Reinhard Klette
    • 2
  1. 1.Department of Computer EngineeringQazvin Islamic Azad UniversityQazvinIran
  2. 2.Department of Electrical and Electronic EngineeringAuckland University of TechnologyAucklandNew Zealand

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