Transportation Safety Improvements Through Video Analysis: An Application of Obstacles and Collision Detection Applied to Railways and Roads

  • Hui Wang
  • Xiaoquan Zhang
  • Lorenzo Damiani
  • Pietro Giribone
  • Roberto RevetriaEmail author
  • Giacomo Ronchetti
Conference paper


Obstacles detection systems are essential to obtain a higher safety level on railways. Such systems has the ability to contribute to the development of automated guided trains. Even though some laser equipments have been used to detect obstacles, short detection distance and low accuracy on curve zones make them not the best solution. In this paper, after an assessment of the risks related to railway accidents and their possible causes, computer vision combined with prior knowledge is used to develop an innovative approach. A function to find the starting point of the rails is proposed. After that, bottom-up adaptive windows are created to focus on the region of interest and ignore the background. The whole system can run in real time thanks to its linear complexity. Experimental tests demonstrated that the system performs well in different conditions.


Computer vision Experimental assessment Obstacles detection Prior knowledge Transportation Transport safety video forensic 


  1. 1.
    H. Wang, X. Zhang, L. Damiani, P. Giribone, R. Revetria, G. Ronchetti, Video analysis for improving transportation safety: obstacles and collision detection applied to railways and roads, in Proceedings of the International MultiConference of Engineers and Computer Scientists 2017. Lecture Notes in Engineering and Computer Science, 15–17 Mar 2017, Hong Kong, pp. 909–915Google Scholar
  2. 2.
    R. Passarella, B. Tutuko, A.P.P. Prasetyo, Design concept of train obstacle detection system in Indonesia. IJRRAS 9(3), 453–460 (2011)Google Scholar
  3. 3.
    F. Kruse, S. Milch, H. Rohling, Multi sensor system for obstacle detection in train applications. Proc. IEEE Trans. 42–46 (2003)Google Scholar
  4. 4.
    E. Briano, C. Caballini, R. Revetria, The maintenance management in the highway context: a system dynamics approach, in Proceedings of FUBUTEC (2009), pp. 15–17; R.W. Lucky, Automatic equalization for digital communication. Bell Syst. Tech. J. 44(4), 547–588 (1965)Google Scholar
  5. 5.
    E. Briano, C. Caballini, R. Revetria, M. Schenone, A. Testa, Use of system dynamics for modelling customers flows from residential areas to selling centers, in Proceedings of the 12th WSEAS International Conference on Automatic Control, Modelling and simulation (World Scientific and Engineering Academy and Society (WSEAS), 2010), pp. 269–273Google Scholar
  6. 6.
    S. Sugimoto, H. Tateda, H. Takahashi, M. Okutomi, Obstacle detection using millimeter-wave radar and its visualization on image sequence, in 2004 Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, vol. 3 (IEEE, 2004) pp. 342–345Google Scholar
  7. 7.
    J. Canny, A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 6, 679–698 (1986)CrossRefGoogle Scholar
  8. 8.
    M. Fang, G.X. Yue, Q.-C. Yu, The study on an application of Otsu method in canny operator, in International Symposium on Information Google Scholar
  9. 9.
    T. Yao, S. Dai, P. Wang, Y. He, Image based obstacle detection for automatic train supervision, in 2012 5th International Congress on Image and Signal Processing (CISP) (IEEE, 2012), pp. 1267–1270; E.H. Miller, A note on reflector arrays (Periodical style—Accepted for publication). Eng. Lett.Google Scholar
  10. 10.
    L. Tong, L. Zhu, Yu. Zujun, B. Guo, Railway obstacle detection using onboard forward-viewing camera. J. Transp. Syst. Eng. Inf. Technol. 4, 013 (2012)Google Scholar
  11. 11.
    L.F. Rodriguez, J.A. Uribe, J.F.V. Bonilla, Obstacle detection over rails using hough transform, in 2012 XVII Symposium of Image, Signal Processing, and Artificial Vision (STSIVA) (IEEE, 2012), pp. 317–322Google Scholar
  12. 12.
    J.C. Espino, B. Stanciulescu, Rail extraction technique using gradient information and a priori shape model, in 2012 15th International IEEE Conference on Intelligent Transportation Systems (ITSC) (IEEE, 2012), pp. 1132–1136Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Hui Wang
    • 1
  • Xiaoquan Zhang
    • 1
  • Lorenzo Damiani
    • 2
  • Pietro Giribone
    • 2
  • Roberto Revetria
    • 2
    Email author
  • Giacomo Ronchetti
    • 2
  1. 1.Ulster UniversityBelfastUK
  2. 2.Genoa UniversityGenoaItaly

Personalised recommendations