Volume 13, Issue 3 (12-2016)                   JSDP 2016, 13(3): 79-98 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

hanifelou Z, Monadjemi A H, moallem P. Robust method of changes of light to detect and track vehicles in traffic scenes. JSDP. 2016; 13 (3) :79-98
URL: http://jsdp.rcisp.ac.ir/article-1-510-en.html
faculty of computer engineering, university of isfahan
Abstract:   (4178 Views)

In this paper, according to the detection and tracking of the moving vehicles at junctions, a rapid method is proposed which is based on intelligent image processing. In the detection part, the Gaussian mixture model has been used to obtain the moving parts. Then, the targets have been detected using HOG features extracted from training images, Ada-boost Cascade Classifier and the trained SVM. At the tracking part, a number of key points on the image of the vehicle were identified at first. The center of mass of the object and the edges were used to obtain these key points because these points are primarily important and more common in tracking rigid bodies. Then, these points were tracked in consecutive frames using definitive adaptive procedures. Also, the Kalman filter has been used to estimate new locations when the detector  is not able to detect the targets. The major advantage of this method  in comparison with the previous methods is its resistance against vehicle's overlapping and changes in Illuminations, so that the detection accuracy is 90.80% on overloaded traffic scenes and 88.75% on the tracking vehicles.

Full-Text [PDF 4066 kb]   (1109 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2016/05/3 | Accepted: 2016/07/27 | Published: 2017/04/23 | ePublished: 2017/04/23

1. [1] Y. Wu, J. Lim, and M.-H. Yang, "Online object tracking: A benchmark," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 2411–2418. [DOI:10.1109/CVPR.2013.312]
2. [2] R. A. Priyadharshini, S. Arivazhagan, and L. Sangeetha, "Vehicle recognition based on Gabor and Log-Gabor transforms," in Advanced Communication Control and Computing Technologies (ICACCCT), 2014 International Conference on, 2014, pp. 1268–1272. [DOI:10.1109/ICACCCT.2014.7019303]
3. [3] Y. Zhu, D. Comaniciu, M. Pellkofer, and T. Köhler, "System and method for vehicle detection and tracking." Google Patents, 2010.
4. [4] D. Balcones, D. F. Llorca, M. A. Sotelo, M. Gavilán, S. Álvarez, I. Parra, and M. Oca-a, "Real-time vision-based vehicle detection for rear-end collision mitigation systems," in Computer Aided Systems Theory-EUROCAST 2009, Springer, 2009, pp. 320–325.
5. [5] A. Akula, N. Khanna, R. Ghosh, S. Kumar, A. Das, and H. K. Sardana, "Adaptive contour-based statistical background subtraction method for moving target detection in infrared video sequences," Infrared Phys. Technol., vol. 63, pp. 103–109, 2014. [DOI:10.1016/j.infrared.2013.12.012]
6. [6] J. Gleason, A. V Nefian, X. Bouyssounousse, T. Fong, and G. Bebis, "Vehicle detection from aerial imagery," in Robotics and Automation (ICRA), 2011 IEEE International Conference on, 2011, pp. 2065–2070. [DOI:10.1109/ICRA.2011.5979853]
7. [7] B. Tian, Y. Li, B. Li, and D. Wen, "Rear-view vehicle detection and tracking by combining multiple parts for complex urban surveillance," Intell. Transp. Syst. IEEE Trans., vol. 15, no. 2, pp. 597–606, 2014. [DOI:10.1109/TITS.2013.2283302]
8. [8] J.-Y. Choi, K.-S. Sung, and Y.-K. Yang, "Multiple vehicles detection and tracking based on scale-invariant feature transform," in Intelligent Transportation Systems Conference, 2007. ITSC 2007. IEEE, 2007, pp. 528–533. [DOI:10.1109/ITSC.2007.4357684]
9. [9] J. Ning, L. Zhang, D. Zhang, and W. Yu, "Joint registration and active contour segmentation for object tracking," Circuits Syst. Video Technol. IEEE Trans., vol. 23, no. 9, pp. 1589–1597, 2013. [DOI:10.1109/TCSVT.2013.2254931]
10. [10] G. Hu, N. Gans, N. Fitz-Coy, and W. Dixon, "Adaptive homography-based visual servo tracking control via a quaternion formulation," Control Syst. Technol. IEEE Trans., vol. 18, no. 1, pp. 128–135, 2010. [DOI:10.1109/TCST.2008.2009227]
11. [11] X. Cao, Z. Shi, P. Yan, and X. Li, "Collaborative Kalman filters for vehicle tracking," in Machine Learning for Signal Processing (MLSP), 2011 IEEE International Workshop on, 2011, pp. 1–6. [DOI:10.1109/MLSP.2011.6064581]
12. [12] Y. Liu, Y. Lu, Q. Shi, and J. Ding, "Optical flow based urban road vehicle tracking," in Computational Intelligence and Security (CIS), 2013 9th International Conference on, 2013, pp. 391–395. [DOI:10.1109/CIS.2013.89]
13. [13] J. Kokkala and S. Särkkä, "Combining particle MCMC with Rao-Blackwellized Monte Carlo data association for parameter estimation in multiple target tracking," Digit. Signal Process., vol. 47, pp. 84–95, 2015. [DOI:10.1016/j.dsp.2015.04.004]
14. [14] A. Jazayeri, H. Cai, J. Y. Zheng, and M. Tuceryan, "Vehicle detection and tracking in car video based on motion model," Intell. Transp. Syst. IEEE Trans., vol. 12, no. 2, pp. 583–595, 2011. [DOI:10.1109/TITS.2011.2113340]
15. [15] C. Wang, "Moving Vehicle Detection Combined Contourlet Transform with Frame Difference in Highways Surveillance Video," in Advances in Electrical Engineering and Electrical Machines, Springer, 2011, pp. 65–71.
16. [16] C.-C. Wong, W.-C. Siu, P. Jennings, S. Barnes, and B. Fong, "A smart moving vehicle detection system using motion vectors and generic line features," Consum. Electron. IEEE Trans., vol. 61, no. 3, pp. 384–392, 2015. [DOI:10.1109/TCE.2015.7298299]
17. [17] Z. Sun, G. Bebis, and R. Miller, "On-road vehicle detection: A review," Pattern Anal. Mach. Intell. IEEE Trans., vol. 28, no. 5, pp. 694–711, 2006. [DOI:10.1109/TPAMI.2006.104] [PMID]
18. [18] H.-H. Lin, J.-H. Chuang, and T.-L. Liu, "Regularized background adaptation: a novel learning rate control scheme for Gaussian mixture modeling," Image Process. IEEE Trans., vol. 20, no. 3, pp. 822–836, 2011. [DOI:10.1109/TIP.2010.2075938] [PMID]
19. [19] P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, 2001, vol. 1, pp. I–511. [DOI:10.1109/CVPR.2001.990517]
20. [20] M. Bertozzi, A. Broggi, M. Del Rose, M. Felisa, A. Rakotomamonjy, and F. Suard, "A pedestrian detector using histograms of oriented gradients and a support vector machine classifier," in Intelligent Transportation Systems Conference, 2007. ITSC 2007. IEEE, 2007, pp. 143–148. [DOI:10.1109/ITSC.2007.4357692]
21. [21] H. Tan, B. Yang, and Z. Ma, "Face recognition based on the fusion of global and local HOG features of face images," Comput. Vision, IET, vol. 8, no. 3, pp. 224–234, 2014. [DOI:10.1049/iet-cvi.2012.0302]
22. [22] A. Yilmaz, O. Javed, and M. Shah, "Object tracking: A survey," Acm Comput. Surv., vol. 38, no. 4, p. 13, 2006. [DOI:10.1145/1177352.1177355]
23. [23] S. Shantaiya, K. Verma, and K. Mehta, "Multiple Object Tracking using Kalman Filter and Optical Flow," Eur. J. Adv. Eng. Technol., vol. 2, no. 2, pp. 34–39, 2015.
24. [24] J. Black, T. Ellis, P. Rosin, and others, "A novel method for video tracking performance evaluation," Proc. IEEE Int. Vis. Surveill. Perform. Eval. Track. Surveill. (VS-PETS 03), pp. 125–132, 2003.
25. [25] L. Oliveira and U. Nunes, "On integration of features and classifiers for robust vehicle detection," in Intelligent Transportation Systems, 2008. ITSC 2008. 11th International IEEE Conference on, 2008, pp. 414–419. [DOI:10.1109/ITSC.2008.4732545]

Add your comments about this article : Your username or Email:

Send email to the article author

© 2015 All Rights Reserved | Signal and Data Processing