Performance Analysis of Vehicle Detection Techniques: A Concise Survey

  • Adnan Hanif
  • Atif Bin Mansoor
  • Ali Shariq Imran
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 746)


Attention towards Intelligent Transportation System (ITS) has increased manifold especially due to prevailing security situation in the past decade. An integral part of ITS is video-based surveillance systems extracting real-time traffic parameters such as vehicle counting, vehicle classification, vehicle velocity etc. using stationary cameras installed on road sides. In all these systems, robust and reliable detection of vehicles is significantly a critical step. Since, several vehicle detection techniques exist, evaluating these techniques with respect to different environment conditions and application scenarios will give a better choice for actual deployment. The paper presents a concise survey of vehicle detection techniques used in diverse applications of video-based surveillance systems. Moreover, three main detection algorithms; Gaussian Mixture Model (GMM), Histogram of Gradients (HoG), and Adaptive motion Histograms based vehicle detection are implemented and evaluated for performance under varying illumination, traffic density and occlusion conditions. The survey provides a ready-reference for preferred vehicle detection technique under different applications.


Vehicle detection Gaussian Mixture Model Histogram of Gradients Performance analysis 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Adnan Hanif
    • 1
  • Atif Bin Mansoor
    • 2
  • Ali Shariq Imran
    • 3
  1. 1.Department of Avionics EngineeringAir UniversityIslamabadPakistan
  2. 2.School of CSSEThe University of Western AustraliaPerthAustralia
  3. 3.Department of Computer ScienceNTNUGjøvikNorway

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