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Reseach Article

Automatic Vehicle Detection and Tracking in Aerial Surveillances using SVM

by Divya Michael, Paul P Mathai, Abhidhat E
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 85 - Number 9
Year of Publication: 2014
Authors: Divya Michael, Paul P Mathai, Abhidhat E
10.5120/14867-2990

Divya Michael, Paul P Mathai, Abhidhat E . Automatic Vehicle Detection and Tracking in Aerial Surveillances using SVM. International Journal of Computer Applications. 85, 9 ( January 2014), 6-12. DOI=10.5120/14867-2990

@article{ 10.5120/14867-2990,
author = { Divya Michael, Paul P Mathai, Abhidhat E },
title = { Automatic Vehicle Detection and Tracking in Aerial Surveillances using SVM },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 85 },
number = { 9 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 6-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume85/number9/14867-2990/ },
doi = { 10.5120/14867-2990 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:02:00.460454+05:30
%A Divya Michael
%A Paul P Mathai
%A Abhidhat E
%T Automatic Vehicle Detection and Tracking in Aerial Surveillances using SVM
%J International Journal of Computer Applications
%@ 0975-8887
%V 85
%N 9
%P 6-12
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Target object detection in aerial surveillance using image processing techniques is growing more and more important. Aerial surveillance is more suitable for monitoring fast moving targets and covers a much larger spatial area. These technologies have a variety of applications, such as traffic management, police and military. Aerial view has the advantage of providing a better perspective of the area being covered and this make use of the aerial videos taken from aerial vehicles. In an automatic vehicle detection system for aerial surveillance background colors are eliminated and then features are extracted. This system extracts features including color feature and local feature points. For vehicle color extraction, system utilizes color transform to separate vehicle colors and non-vehicle colors effectively. For edges detection, system applies moment-preserving method to adjust the thresholds for canny edge detector automatically, which improves the adaptability and accuracy of the system. A support Vector Machine is constructed for classification purpose.

References
  1. Hsu-Yung Cheng, Chih-Chia Weng, and Yi-Ying Chen" Vehicle Detection in Aerial urveillance Using Dynamic Bayesian Networks. "IEEE Trans. On Image Processing, Vol. 21, No. 4, April 2012.
  2. C Stauffer, W Grimson, "Adaptive background mixture models for real - time tracking". Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, 2(6) : 248 – 252.
  3. S. Srinivasan, H. Latchman, J. Shea, T. Wong, and J. Mc Nair, ?Airborne traffic surveillance systems: Video surveillance of highway traffic, in Pro. ACm 2nd Int. Workshop Video Surveillance Sens. Netw. , 2004,pp. 131-135.
  4. S. S. Hinz and A. Baumgartner, ?Vehicle detection in aerial images using generic features, grouping, and context, in Proc. DAGM-Symp. , Sep. 2001, vol. 2191, Lecture Notes in Computer Science, pp. 45–52.
  5. H. Cheng and J. Wus, ?Adaptive region of interest estimation for aerial surveillance video, in Proc. IEEE Int. Conf. Image Process. , 2005, vol. 3, pp. 860–863.
  6. R. Lin, X. Cao, Y. Xu, C. Wu, and H. Qiao, ?Airborne moving vehicle detection for urban traffic surveillance,in Proc. 11th Int. IEEE Conf. Intell. Transp. Syst. , Oct. 2008, pp. 163–167
  7. H. Cheng and D. Butler, ?Segmentation of aerial surveillance video using a mixture of experts, in Proc. IEEE Digit. Imaging Comput. —Tech. Appl. , 2005, p. 66.
  8. J. Y. Choi and Y. K. Yang, ?Vehicle detection from aerial images using local shape information, Adv. Image Video Technol. , vol. 5414, Lecture Notes in Computer Science, pp. 227–236, Jan. 2009.
  9. L. W. Tsai, J. W. Hsieh, and K. C. Fan, "Vehicle detection using normalized color and edge map," IEEE Trans. Image Process. , vol. 16, no. 3, pp. 850–864, Mar. 2007.
  10. B. Morris and M. Trivedi, ?Robust classification and tracking of vehicles in traffic video streams, Proc. IEEE ITSC, 2006, pp. 1078–1083.
  11. J. W. Hsieh, S. H. Yu, Y. S. Chen, and W. F. Hu, Automatic traffic surveillance system for vehicle tracking and classification, IEEE Trans. Intell. Transp. Syst. , vol. 7, no. 2, pp. 175–187, Jun. 2006.
  12. Z. Zivkovic Improved adaptive gaussian mixture model for background subtraction. Int'l Conf. on Pattern Recognition, 2:28–31, 2004.
  13. D. -S. Lee. Effective gaussian mixture learning for video background subtraction. IEEE Trans. on Pattern Analysis and Ma-chine Intelligence, 27:827–832, 2005.
  14. C. G. Harris and M. J. Stephens,"A combined corner and edge detector," ?inProc. 4th Alvey Vis. Conf. , 1988, pp. 147–151.
  15. J. F. Canny,A computational approach to edge detection, IEEE Trans. Pattern Anal. Mach. Intell. , vol. PAMI-8, no. 6, pp. 679–698,Nov. 1986.
  16. W. H. Tsai, ?Moment-preservingthresholding: A new approach, Comput. Vis. Graph. , Image Process. , vol. 29, no. 3, pp. 377–393, 1985.
  17. N. Cristianini and J. Shawe-Taylor,An Introduction to Support VectorMachines and Other Kernel-Based Learning Methods. Cambridge,U. K. : Cambridge Univ. Press, 2000.
  18. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2nd ed. Englewood Cliffs, NJ: Prentice-Hall, 2003.
  19. Alan. J. Lipton,H. Fujiyoshi, Raju. S. Patil ,"M oving Target Classification and Tracking from Real-time Video", WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98) Page 8.
  20. Wenhao Lu, Shengjin Wang, XioaqingDing,Vehicle Detection and Tracking in Relatively Crowded Conditions,Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics San Antonio, TX, USA.
Index Terms

Computer Science
Information Sciences

Keywords

Aerial Surveillances Training Detection Classification SVM GMM.