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
Article

Abstract

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.

Keywords

Video surveillance Feature extraction SVM Obstacle detection 

References

  1. 1.
    del Rincon, J.M., Herrero Jaraba, J, Gomez, J.R., et al. (2006). Automatic left luggage detection and tracking using multi-camera. IEEE International Workshop on Performance Evaluation in Tracking and Surveillance (pp. 59–66).Google Scholar
  2. 2.
    Krahnstoever N., Tu P., Sebastian T., et al. (2006). Mutli-view detection and tracking of travelers and luggage in mass transit environments. 9th PETS, CVPR (pp. 67–74).Google Scholar
  3. 3.
    Singh, A., Sawan, S., Hanmandlu, M, et al. (2009). An abandoned object detection system based on dual background segmentation. 6th IEEE International Conference on Advanced Video and Signal Based Surveillance (pp. 352–357).Google Scholar
  4. 4.
    Grabner, H, Roth, P., Grabner, M. (2006). Autonomous learning of a robust background model for change detection. IEEE International Workshop on PETS (pp. 39–54).Google Scholar
  5. 5.
    YingLi, T., Feris, R. S., Liu, H., et al. (2011). Robust detection of abandoned and removed objects in complex surveillance videos. IEEE Transactions on Systems, Man, and Cybernetics Part C: Applications and Reviews, 41(5), 565–576.CrossRefGoogle Scholar
  6. 6.
    Xiya, L., Jingling, W., & Qin, Z. (2012). An abandoned object detection system based on dual background and motion analysis. IEEE International Conference on Computer Science & Service System (pp. 2293–2296).Google Scholar
  7. 7.
    Muchtar, K., Lin, C-Y, Kang, L-W, et al. (2013). Abandoned object detection in complicated environments. Signal and Information Processing Association Annual Summit and Conference. Asia-Pacific.Google Scholar
  8. 8.
    Porikli, F., Ivanov, Y. & Haga, T., (2008). Robust abandoned object detection using dual foregrounds. Journal on Advances in Signal Processing, 30.Google Scholar
  9. 9.
    Spengler, M., & Schiele, B. (2003). Automatic detection and tracking of abandoned objects. IEEE International Workshop on Visual Surveillance and PETS.Google Scholar
  10. 10.
    Liao, H.H., Chang , J.Y. & Chen, L.G. (2008). A localized approach to abandoned luggage detection with foreground-mask sampling. IEEE Proceedings of Advanced Video and Signal Based Surveillance (pp. 132–139).Google Scholar
  11. 11.
    Fu, H., Xiang, M., Ma, H., et al. (2011). Abandoned object detection in highway scene. 6th IEEE International Conference on Pervasive Computing and Applications (pp. 117–121).Google Scholar
  12. 12.
    Franke, U., & Heinrich, S. (2002). Fast obstacle detection for urban traffic situations. IEEE Transactions on Intelligent Transportation Systems, 3(3), 173–181.CrossRefGoogle Scholar
  13. 13.
    Nedevschi, S., & Danescu, R, et al. (2004). High accuracy stereo vision system for far distance obstacle detection. IEEE Intelligent Vehicles Symposium (pp. 292–297), Parma.Google Scholar
  14. 14.
    Sacchi, C., & Regazzoni, C. S. (2013). A distributed surveillance system for detection of abandoned objects in unmanned railway environments. IEEE Transactions on Vehicular Technology, 49(5), 2013–2026.CrossRefGoogle Scholar
  15. 15.
    Morimoto, K. (1994). System for detecting and warning an illegally parked vehicle. U.S. Patent 5343237.Google Scholar
  16. 16.
    Boragno, S., Boghossian, B., Black, J., Makris, D., & Velastin, S. (2007). A DSP-based system for the detection of vehicles parked in prohibited areas. In Proc. IEEE Int. Conf. Advanced Video Signal Based Surveillance (pp. 260–265).Google Scholar
  17. 17.
    Lee, J.T., Ryoo, M.S., Riley, M. & Aggarwal, J.K. (2007). Real-time detection of illegally parked vehicles using 1-D transformation. In Proc. IEEE Int. Conf. Advanced Video Signal Based Surveillance (pp. 254–259).Google Scholar
  18. 18.
    Bevilacqua, A., & Vaccari, S. (2007). Real time detection of stopped vehicles in traffic scenes. In Proc. IEEE Int. Conf. Advanced Video Signal Based Surveillance (pp. 266–270), London.Google Scholar
  19. 19.
    Kimachi, M., Kanayama, K., & Teramoto, K. (1994). Incident prediction by fuzzy image sequence analysis. In Proc. IEEE int. Conference Vehicle Navigation and Information Systems (pp. 51–57).Google Scholar
  20. 20.
    Zeng, D., Xu, J., & Xu, G. (2008). Data fusion for traffic incident detection using D-S evidence theory with probabilistic SVMs. Journal of Computers, 3(10), 36–43.CrossRefGoogle Scholar
  21. 21.
    Ikeda, H., Matsuo, T., Kaneko, Y., & Tsuji, K. (1999). Abnormal incident detection system employing image processing technology. Proc. of the IEEE Conference Vehicle Navigation and Information, Systems (pp. 748–752), Tokyo.Google Scholar
  22. 22.
    Meler, M. (2006). Car color and logo recognition. CSE 190A Projects in Vision and Learning. University of California.Google Scholar
  23. 23.
    Toth, D., & Aach, T. (2003). Detection and recognition of moving objects using statistical motion detection and Fourier descriptors. 12th International Conference on Image Analysis and Processing (pp. 430–435).Google Scholar
  24. 24.
    Zhang, L., Li, S.Z., Yuan, X., & Xiang, S. (2007). Real-time object classification in video surveillance based on appearance learning. IEEE Conference on Computer Vision and Pattern Recognition (pp. 1–8).Google Scholar
  25. 25.
    Gavrila, D. (2000). Pedestrian detection from a moving vehicle. Proceedings of the 6th European Conference on Computer Vision-Part II (pp. 37–49).Google Scholar
  26. 26.
    Gavrila, D.M., Giebel, J., & Munder, S. (2004). Vision-based pedestrian detection: the PROTECTOR system. IEEE in Intelligent Vehicles Symposium (pp. 13–18).Google Scholar
  27. 27.
    Khammari, A., Nashashibi, F., Abramson, Y., & Laurgeau¸ C. (2005). Vehicle detection combining gradient analysis and AdaBoost classification. IEEE Proceedings in Intelligent Transportation Systems (pp. 66–71).Google Scholar
  28. 28.
    Ikeda H., Kaneko, Y., Matsuo, T., et al. (1999). Abnormal incident detection system employing image processing technology. Towards the New Horizon Together. Proceedings of the 5th World Congress on Intelligent Transport Systems (pp. 748–752), Seoul.Google Scholar
  29. 29.
    Bhargava, M., Chia-Chih, Chen., Ryoo, M.S., & Aggarwal, J.K. (2007). Detection of abandoned objects in crowded environments. IEEE Conference on Advanced Video and Signal Based Surveillance (pp. 271–276), London.Google Scholar
  30. 30.
    Stauffer, C., & Grimson, W.E.L. (1999). Adaptive background mixture models for real-time tracking. IEEE Conference on Computer Vision and Pattern Recognition (pp. 246–252).Google Scholar
  31. 31.
    Xuehua, S., Jingzhu, C., Chong, H., & Xiang, Z. (2010). A robust moving objects detection based on improved gaussian mixture model. International Conference on Artificial Intelligence and Computational Intelligence (pp. 54–58), Sanya.Google Scholar
  32. 32.
    Zivkovic, Z., & van der Heijden, F. (2004). Recursive unsupervised learning of finite mixture models. IEEE Transaction on Pattern Analysis and Machine Intelligence, 26(5), 651–656.CrossRefGoogle Scholar
  33. 33.
    Khudeev, R. (2005). A New flood-fill algorithm for closed contour. IEEE International Siberian Conference on Control and Communications (pp. 172–176), Tomsk.Google Scholar
  34. 34.
    Bradski, G.R., Clara, S. (1998). Computer vision face tracking for use in a perceptual user interface. Intel Technology Journal, 1–15.Google Scholar
  35. 35.
    Comaniciu, D., Ramesh, V.,& Meer, P. (2000). Real-time tracking of non-rigid objects using mean shift. IEEE Conference on Computer Vision and Pattern Recognition (pp. 142–149), Hilton Head Island.Google Scholar

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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