An Improved Vehicle Detection and Tracking Model

  • Libin Hu
  • Zhongtao LiEmail author
  • Hao Xu
  • Bei Fang
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 127)


With the continuous advancement of urbanization in China, the number of urban motor vehicles has increased exponentially, but the pressure on urban traffic and security monitoring has also increased. In recent years, the application of Intelligent Transportation System (ITS) has created good economic benefits for the transportation industry. Vehicle Detection and Tracking is the basis for subsequent vehicle information attribute calculation and statistical analysis of data and plays a leading role in ITS. This paper discusses the state-of-the-art detection and tracking algorithms and points out their shortcomings, and on this basis, we proposed an improved video vehicle detection and tracking model: a model based on Gaussian mixture model, Kalman filter + CAMshift + contour method. First, in the background extraction process, the Gaussian mixture model with different parameters is used for the foreground and the background to reduce the calculation cost and enhance the separation effect; For the extracted foreground, the part connecting in different vehicles is overlapped by the matching of the pits to distinguish the overlapping vehicles; In the tracking process, the results of CAMshift and contour tracking are used as observations of the Kalman filter to avoid the phenomenon that vehicles with similar color distribution characteristics and similar positions are misidentified as the same target. The experimental results show that our model has better robustness to complex road conditions, and the detection and tracking effect is better than other models.


Video vehicle detection and tracking Mixed gaussian model Overlapping segmentation Weighted CAMshift 


  1. 1.
    Chen, Y.: Service-Oriented Computing and System Integration: Software, IoT, Big Data, and AI as Services, 6th edn. Kendall Hunt Publishing (2018)Google Scholar
  2. 2.
    Chen, Y., Hualiang, H.: Internet of intelligent things and robot as a service. Simul. Modell. Pract. Theory 34, 159–171 (2013)CrossRefGoogle Scholar
  3. 3.
    Pauer, G.: Development potentials and strategic objectives of intelligent transport systems improving road safety. Transp. Telecommun. J. 18(1) (2017)CrossRefGoogle Scholar
  4. 4.
    Oskarbski, J., Jamroz, K.: Reliability and safety as an objective of intelligent transport systems in urban areas. J. KONBiN, 34(1) (2015)CrossRefGoogle Scholar
  5. 5.
    Abutaleb, A.S.: Automatic thresholding of gray level pictures using two-dimensional entropy. Comput. Vis. Graph. Image Process. 47(2), 22–32 (1989)CrossRefGoogle Scholar
  6. 6.
    Park, Y.: Shape resolving local thresholding for object detection. Pattern Recogn. Lett. 22(5), 883–890 (2001)CrossRefGoogle Scholar
  7. 7.
    Piao, S., Sutjaritvorakul, T.: Compact data association in multiple object tracking: pedestrian tracking on mobile vehicle as case study. IFAC PapersOnLine, 49(15) (2016)CrossRefGoogle Scholar
  8. 8.
    Mayyas, A.R., Kumar, S., Pisu, P., Rios, J., Jethani, P.: Model-based design validation for advanced energy management strategies for electrified hybrid power trains using innovative vehicle hardware in the loop (VHIL) approach. Appl. Energy, 204 (2017)CrossRefGoogle Scholar
  9. 9.
    Hadi, R.A., George, L.E., Mohammed, M.J.: A computationally economic novel approach for real-time moving multi-vehicle detection and tracking toward efficient traffic surveillance. Arab. J. Sci. Eng. 42(2) (2017)CrossRefGoogle Scholar
  10. 10.
    Koller, Y.D., Daniilidis, Y.K., Nagel, Y.Z.H., et al.: Model based object tracking in monocular image sequences of road traffic scenes. Int. J. Comput. Vis. 10(3), 257–281 (1993)CrossRefGoogle Scholar
  11. 11.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, pp. 246–252 (1999)Google Scholar
  12. 12.
    Kustikova, V.D., Gergel, V.P.: Vehicle video detection and tracking quality analysis. Pattern Recognition and Image Analysis, 26(1) (2016)CrossRefGoogle Scholar
  13. 13.
    Yuan, B., Zhang, Y.: Traffic congestion detection algorithm based on image texture analysis. J. Shanghai Ship Shipp. Res. Inst. 38(4) (2015)Google Scholar
  14. 14.
    Zhou, S.K., Chellappa, R., Moghaddam, B.: Visual tracking and recognition using appearance adaptive models in particle filters. IEEE Trans. Image Process. 13(11), 1491–1506 (2004)CrossRefGoogle Scholar
  15. 15.
    Rathi, Y., Vaswani, N., Tannenbaum, A., et al.: Tracking deforming objects using particle filtering for geometric active contours. IEEE Trans. Pattern Anal. Mach. Intell. 29(8), 1470–1475 (2007)CrossRefGoogle Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Information Science and EngineeringUniversity of JinanJinanChina
  2. 2.Shandong Provincial Key Laboratory of Network Based Intelligent ComputingUniversity of JinanJinanChina

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