Vehicle Detection and Tracking at Night in Video Surveillance

Authors

  • Qing Tian North China University of Technology
  • Long Zhang North China University of Technology
  • Yun Wei Southeast University, and Beijing Urban Engineering Design and Research Institute
  • Wenhua Zhao North China University of Technology
  • Weiwei Fei CSSC

DOI:

https://doi.org/10.3991/ijoe.v9iS6.2828

Abstract


This Many detection and tracking methods are able to detect and track vehicle motion reliably in the daytime. However, vehicle detection and tracking in video surveillance at night remain very important problems that the vehicle signatures have low contrast against the background. Traditional methods based on the analysis of the difference between successive frames and a background frame can not work. This paper presents a method for vehicle detection and tracking at night in video surveillance. The method uses Histograms of Oriented Gradients (HOG) features to extract features, and then uses Support Vector Machine (SVM) to recognize the object. In tracking phase, we use Kalman filter to track the object. As shown in experiments, the algorithm can exactly detect and track moving vehicles in video surveillance at night.

Author Biographies

Qing Tian, North China University of Technology

College of Information Engineering

Long Zhang, North China University of Technology

College of Information Engineering

Yun Wei, Southeast University, and Beijing Urban Engineering Design and Research Institute

telligent Transportation System Research Center

Wenhua Zhao, North China University of Technology

College of Information Engineering

Weiwei Fei, CSSC

Systems Engineering Research Institute

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Published

2013-06-26

How to Cite

Tian, Q., Zhang, L., Wei, Y., Zhao, W., & Fei, W. (2013). Vehicle Detection and Tracking at Night in Video Surveillance. International Journal of Online and Biomedical Engineering (iJOE), 9(S6), pp. 60–64. https://doi.org/10.3991/ijoe.v9iS6.2828

Issue

Vol. 9 (2013): Special Issue "AIAIP2012"

Section

Special Focus Papers

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