Vehicle Detection and Tracking Based on GMM and Enhanced Camshift Algorithm

Kaiyang Zhong; Zhaoyang Zhang; Zhengyu Zhao

doi:doi:10.11648/j.jeee.20180602.11

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Vehicle Detection and Tracking Based on GMM and Enhanced Camshift Algorithm

Kaiyang Zhong, Zhaoyang Zhang, Zhengyu Zhao

Published in Journal of Electrical and Electronic Engineering (Volume 6, Issue 2)

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.

Published in	Journal of Electrical and Electronic Engineering (Volume 6, Issue 2)
DOI	10.11648/j.jeee.20180602.11
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

[1]	"Intelligent Transportation Systems Joint Program Office", United States Department of Transportation, vol. 10, November 2016.
[2]	S. Sivaraman, and M. M. Trivedi, “Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis,” IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, pp. 1773-1795, 2013.
[3]	X. Wang, “Intelligent multicamera video surveillance: A review,” Pattern recognition letters, vol. 34, no. 1, pp. 3- 19, 2013.
[4]	M. A. Manzoor, Y. Morgan, "Vehicle Make and Model Classification System using Bag of SIFT Features", 7th IEEE Annual Conference on Computing and Communication Workshop and Conference (CCWC), vol. 02, pp. 572-577, March 2017.
[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.
[7]	M. Atibi, I. Atouf, M. Boussaa, A. Bennis, "Real-time detection of vehicle using the haar-like features and artificial neuron networks", Proc. Computer Science, vol. 73, pp. 24-31, 2015.
[8]	Gao Lei, "Based on the optical flow in the dynamic scene of the vehicle detection and tracking algorithm [D]", University of Science and Technology of China, 2014.
[9]	Junpeng Zhang, Xiuping Jia, Jiankun Hu, "Motion Flow Clustering for Moving Vehicle Detection from Satellite High Definition Video", 2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 29 Nov.-1 Dec. 2017.
[10]	N. Dalal, and B. Triggs, “Histograms of oriented gradients for human detection,”in proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, Jun. 2005, pp. 886-893.
[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.
[20]	K. V. Arya, Shailendra Tiwari, Saurabh Behwal, “Real-time Vehicle Detection and Tracking”, Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2016 13th International Conference on.

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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
N1  - https://doi.org/10.11648/j.jeee.20180602.11
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

Kaiyang Zhong

Department of Software Engineering, Xiamen University, Xiamen, China
Zhaoyang Zhang

Department of Software Engineering, Xiamen University, Xiamen, China
Zhengyu Zhao

Department of Software Engineering, Xiamen University, Xiamen, China

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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

Copy | Download

@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}
}

Copy | Download

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
N1  - https://doi.org/10.11648/j.jeee.20180602.11
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  -

Copy | Download