Journal of Electrical and Electronic Engineering

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Vehicle Detection and Tracking Based on GMM and Enhanced Camshift Algorithm

Received: 26 April 2018    Accepted:     Published: 27 April 2018
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Abstract

Vehicle detection and tracking is an important part of the intelligent transportation system. With the rapid development of computer vision, video based vehicle detection and tracking technology has become a hot topic. In this paper, on the foundation of the present work, an enhanced detection tracking algorithm is proposed based on the popular Gauss mixture model(GMM) and Camshift. First, GMM is used to extract the foreground, and then the morphological operations is carried out to enhance the image, so that to remove the random noises. Finally, enhanced Camshift is designed to track the vehicle which is discussed in detail below. The experimental results demonstrate that the tracking accuracy can be improved.

DOI 10.11648/j.jeee.20180602.11
Published in Journal of Electrical and Electronic Engineering (Volume 6, Issue 2, April 2018)
Page(s) 40-45
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Vehicle Detection, Vehicle Tracking, GMM, Camshift

References
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[5] S. M. Elkerdawi, R. Sayed, and M. ElHelw, “Real-time vehicle detection and tracking using Haar-like features and compressive tracking,” in 1st Iberian Robotics Conference, Jan. 2014, pp. 381-390. Springer International Publishing.
[6] Honghong Yang, Shiru Qu, "Real-time vehicle detection and counting in complex traffic scenes using background subtraction model with low-rank decomposition", IET Intelligent Transport Systems, vol. 12, pp. 75-85, January 2018.
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[11] H. Bay, T. Tuytelaars, and L. Van Gool, “Surf: Speeded up robust features,” in Computer vision (ECCV), Jan. 2006, pp. 404-417. Springer Berlin Heidelberg.
[12] Y. Du, and F. Yuan, “Real-time vehicle tracking by Kalman filtering and Gabor decomposition,” in 1st International Conference on Information Science and Engineering (ICISE), Dec. 2009, pp. 1386-1390.
[13] H. T. Niknejad, A. Takeuchi, S. Mita, and D. McAllester, “On-road multivehicle tracking using deformable object model and particle filter with improved likelihood estimation,” IEEE Transactions on Intelligent Transportation Systems, vol. 13, no. 2, pp. 748-758, 2012.
[14] L. Wei, X. Xudong, W. Jianhua, Z. Yi, and H. Jianming, “A SIFT-based mean shift algorithm for moving vehicle tracking,” in Proc. IEEE Intelligent Vehicles Symposium, Jun. 2014, pp. 762-767.
[15] Kaijing Shi, Hong Bao, Nan Ma,"Forward Vehicle Detection Based on Incremental Learning and Fast R-CNN" in 2017 13th International Conference on Computational Intelligence and Security (CIS).
[16] Alireza Asvadi, Luis Garrote, Cristiano Premebida, Paulo Peixoto, Urbano J. Nunes,"DepthCN: Vehicle detection using 3D-LIDAR and ConvNet" in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 16-19 Oct. 2017.
[17] Flaviu Ionut Vancea, Arthur Daniel Costea, Sergiu Nedevschi, "Vehicle taillight detection and tracking using deep learning and thresholding for candidate generation", 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), 7-9 Sept. 2017.
[18] Z. Wang, and K. Hong, “A new method for robust object tracking system based on scale invariant feature transform and camshift,” in Proc. 2012 ACM Research in Applied Computation Symposium, Oct. 2012, pp. 132-136.
[19] Rhen Anjerome Bedruz, Edwin Sybingco, Argel Bandala, Ana Riza Quiros, Aaron Christian Uy, Elmer Dadios, "Real-time vehicle detection and tracking using a mean-shift based blob analysis and tracking approach", 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 1-3 Dec. 2017.
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Cite This Article
  • APA Style

    Kaiyang Zhong, Zhaoyang Zhang, Zhengyu Zhao. (2018). Vehicle Detection and Tracking Based on GMM and Enhanced Camshift Algorithm. Journal of Electrical and Electronic Engineering, 6(2), 40-45. https://doi.org/10.11648/j.jeee.20180602.11

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    ACS Style

    Kaiyang Zhong; Zhaoyang Zhang; Zhengyu Zhao. Vehicle Detection and Tracking Based on GMM and Enhanced Camshift Algorithm. J. Electr. Electron. Eng. 2018, 6(2), 40-45. doi: 10.11648/j.jeee.20180602.11

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    AMA Style

    Kaiyang Zhong, Zhaoyang Zhang, Zhengyu Zhao. Vehicle Detection and Tracking Based on GMM and Enhanced Camshift Algorithm. J Electr Electron Eng. 2018;6(2):40-45. doi: 10.11648/j.jeee.20180602.11

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  • @article{10.11648/j.jeee.20180602.11,
      author = {Kaiyang Zhong and Zhaoyang Zhang and Zhengyu Zhao},
      title = {Vehicle Detection and Tracking Based on GMM and Enhanced Camshift Algorithm},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {6},
      number = {2},
      pages = {40-45},
      doi = {10.11648/j.jeee.20180602.11},
      url = {https://doi.org/10.11648/j.jeee.20180602.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20180602.11},
      abstract = {Vehicle detection and tracking is an important part of the intelligent transportation system. With the rapid development of computer vision, video based vehicle detection and tracking technology has become a hot topic. In this paper, on the foundation of the present work, an enhanced detection tracking algorithm is proposed based on the popular Gauss mixture model(GMM) and Camshift. First, GMM is used to extract the foreground, and then the morphological operations is carried out to enhance the image, so that to remove the random noises. Finally, enhanced Camshift is designed to track the vehicle which is discussed in detail below. The experimental results demonstrate that the tracking accuracy can be improved.},
     year = {2018}
    }
    

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  • TY  - JOUR
    T1  - Vehicle Detection and Tracking Based on GMM and Enhanced Camshift Algorithm
    AU  - Kaiyang Zhong
    AU  - Zhaoyang Zhang
    AU  - Zhengyu Zhao
    Y1  - 2018/04/27
    PY  - 2018
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    DO  - 10.11648/j.jeee.20180602.11
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
    SP  - 40
    EP  - 45
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20180602.11
    AB  - Vehicle detection and tracking is an important part of the intelligent transportation system. With the rapid development of computer vision, video based vehicle detection and tracking technology has become a hot topic. In this paper, on the foundation of the present work, an enhanced detection tracking algorithm is proposed based on the popular Gauss mixture model(GMM) and Camshift. First, GMM is used to extract the foreground, and then the morphological operations is carried out to enhance the image, so that to remove the random noises. Finally, enhanced Camshift is designed to track the vehicle which is discussed in detail below. The experimental results demonstrate that the tracking accuracy can be improved.
    VL  - 6
    IS  - 2
    ER  - 

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Author Information
  • Department of Software Engineering, Xiamen University, Xiamen, China

  • Department of Software Engineering, Xiamen University, Xiamen, China

  • Department of Software Engineering, Xiamen University, Xiamen, China

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