Journal of Signal Processing Systems

, Volume 82, Issue 3, pp 357–371 | Cite as

Real-Time Automatic Obstacle Detection method for Traffic Surveillance in Urban Traffic

  • Jinhui Lan
  • Yaoliang Jiang
  • Guoliang Fan
  • Dongyang Yu
  • Qi Zhang


Obstacle detection in urban traffic is a hot topic in intelligent visual surveillance systems. In this paper, a real-time automatic obstacle recognition method based on computer vision technology is presented. The proposed method aims at detecting and recognizing the road obstacles such as abandoned objects, accident vehicles and illegally parked vehicles, which can prevent the traffic accident effectively. In the method, the target images are captured by a visible image sensor firstly. In order to avoid the static objects disappearing from foreground in short time when using GMM (Gaussian Mixture Model), background is built and foreground objects are extracted by the proposed algorithm SUOG (Selective Updating of GMM). Relative object speed is used to detect the static obstacles, and FROI (Flushed Region of Interest) algorithm based on the concept of connected domain, is presented to eliminate noises outside road and improve real-time capability. At last, a classification method of adaptive interested region based on HOG and SVM, and a new recognition algorithm of accident vehicles based on multi-feature fusion are proposed to classify the road obstacles. Experiments indicate that the detection rate of the proposed obstacle detection method is up to 96 % in urban road traffic. Through experiment, it is shown that the developed obstacle detection method has low computational complexity, and can fulfill the requirement of real-time applications, and it is correct and effective.


Video surveillance Feature extraction SVM Obstacle detection 



The authors would like to thank Yiliang Zeng, Jian Li, Min Liu of the School of Automation and Electrical Engineering University of Science and Technology Beijing, for their great help. This paper is supported by National Natural Science Foundation of China (Grant No. 61174181) and Research Fund (Grant No. 9140A05030214QT02070).

Conflict of interests

The authors declare that there is no conflict of interests regarding the publication of this article.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Department of Instrument Science and TechnologyUniversity of Science and Technology BeijingBeijingPeople’s Republic of China
  2. 2.Department of Instrument Science and TechnologyUniversity of Science and Technology BeijingBeijingChina
  3. 3.Visual Computing and Image Processing Laboratory (VCIPL)School of Electrical and Computer Engineering at Oklahoma State University (OSU)StillwaterUSA
  4. 4.Department of Intelligent Science and TechnologyUniversity of Science and Technology BeijingBeijingChina

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