International Conference on Internet of Vehicles

Internet of Vehicles - Safe and Intelligent Mobility pp 164-175 | Cite as

Nighttime Vehicle Detection for Heavy Trucks

  • Zhijuan Zhang
  • Hui Chen
  • Zhiguang Xiao
  • Linlin Chen
  • Reinhard Klette
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9502)


This paper presents a method for detecting vehicles at nighttime, particularly for an application in heavy trucks. Researchers suggested detecting vehicles at nighttime based on symmetry of taillights, or by training a classifier. The headlights of heavy trucks are very bright which causes that taillights of a vehicle in front appear as being asymmetrical. The bright headlight also defines disturbing information that affects the training of a classifier. We propose an improved threshold algorithm that can effectively remove most non-taillight regions and strengthen the shapes of taillight pairs. Positive and negative samples are extracted from these thresholded (pre-processed) images to train a vehicle classifier by using Haar-like features and AdaBoost. We then detect vehicles in these pre-processed images. Experiments are performed using two alternative methods. Results show that our method, i.e. combining threshold pre-processing and training a classifier, is more accurate and robust than the other two methods.



This work is supported by the Natural Science Found of Shandong NSFSD under No. ZR2013FM032.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zhijuan Zhang
    • 1
  • Hui Chen
    • 1
  • Zhiguang Xiao
    • 1
  • Linlin Chen
    • 2
  • Reinhard Klette
    • 3
  1. 1.School of Information Science and EngineeringShandong UniversityJinanChina
  2. 2.China National Heavy Duty Truck Group Co., Ltd.JinanChina
  3. 3.School of EngineeringAuckland University of TechnologyAucklandNew Zealand

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