Vehicle speed and dimensions estimation using on-road cameras by identifying popular vehicles

Document Type : Article

Authors

Faculty of Electrical Engineering, Shahrood University of Technology, Shahrood, Iran

Abstract

One of the major issues related to traffic monitoring systems is analyzing the behavior of vehicles and identifying their characteristics. In this paper, an automated algorithm is proposed for calibration of the road cameras. This calibration is used to estimate the speed and dimensions of the passing vehicles. In this method, a motion plane is obtained in the initial frames according to the direction of moving vehicles. After modeling the background, removing shadows, and identifying the exact boundaries of passing vehicles, a number of common vehicles are recognized by the Bag of Words method. Given the actual metric dimensions of these vehicles and the equivalent dimensions on the motion plane, the metric coefficients are calculated and the calibration process is completed. Each passing vehicle is projected on the motion plane, and speed and dimensions are calculated by tracking it along the road. To test the accuracy of the proposed method, we constructed a ground-truth video dataset, by simultaneous capturing the road vehicles using RGB and speed cameras. Furthermore, to identify common vehicles, a dataset of vehicle images was collected. The proposed method evaluated on our dataset and the mean error of 1.15 km/h is achieved. Accordingly, it outperforms previous methods.

Keywords

References

References:
1. Mosayebi, A. and Khosravi, H. "A simple approach for real time speed estimation of on road vehicles using video sequences", First International Conference in applied research on Electrical, Mechanical and Mechatronics Engineering (2016).
2. Hua, S., Kapoor, M., and Anastasiu, D.C. "Vehicle tracking and speed estimation from traffic videos", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 153- 160 (2018).
3. Ratajczak, R., Domanski, M., andWegner, K. "Vehicle size estimation from stereoscopic video", 2012 19th International Conference on Systems, Signals and Image Processing (IWSSIP), IEEE., pp. 405-408 (2012).
4. Biglari, M., Soleimani, A., and Hassanpour, H. "Partbased recognition of vehicle make and model", IET Image Processing, 11(7), pp. 483-491 (2017).
5. Asgarian Dehkordi, R. and Khosravi, H. "Vehicle Type Recognition based on Dimension Estimation and Bag of Word Classification", Journal of AI and Data Mining, 8(3), pp. 427-438 (2020).
6. Kim, Y. and Kum, D. "Deep learning based vehicle position and orientation estimation via inverse perspective mapping image", 2019 IEEE Intelligent Vehicles Symposium (IV), IEEE, pp. 317-323 (2019).
7. Dubska, M., et al. "Fully automatic roadside camera calibration for traffic surveillance", IEEE Transactions on Intelligent Transportation Systems, 16(3), pp. 1162-1171 (2014).
8. Dawson, D.N. and Birchfield, S.T. "An energy minimization approach to automatic traffic camera calibration", IEEE Transactions on Intelligent Transportation Systems, 14(3), pp. 1095-1108 (2013).
9. Sochor, J., Juranek, R., and Herout, A., et al. "Traffic surveillance camera calibration by 3d model bounding box alignment for accurate vehicle speed measurement", Computer Vision, 161, pp. 87-98 (2017).
10. Zhang, J., Xiao, W., Coifman, B., et al. "Vehicle tracking and speed estimation from roadside lidar", IEEE Journal of Selected Topics in Applied Earth Observations, 13, pp. 5597-5608 (2020).
11. Dong, H., Wen, M., and Yang, Z. "Vehicle speed estimation based on 3D ConvNets and non-local blocks", Future Internet, 11(6), p. 123 (2019).
12. Wang, W., Yang, S., Li, Y., et al. "A rough vehicle distance measurement method using monocular vision and license plate", 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), IEEE., pp. 426-430 (2015).
13. Xu, H. and Wang, X. "Camera calibration based on perspective geometry and its application in LDWS", Physics Procedia, 33, pp. 1626-1633 (2012).
14. Eslami, H., Raie, A., and Faez, K. "Precise vehicle speed measurement for law enforcement applications based on calibrated camera with parallel standard patterns", IET Computer Vision, 10(5), pp. 398-406 (2016).
15. Zhan, X., Zheng, Y., Yi, X., et al. "Citywide traffic volume estimation using trajectory data", IEEE Transactions on Knowledge, 29(2), pp. 272-285 (2016).
16. You, X. and Zheng, Y. "An accurate and practical calibration method for roadside camera using two vanishing points", Neurocomputing, 204, pp. 222-230 (2016).
17. Filipiak, P., Golenko, B., and Dolega, C. "NSGAII based auto-calibration of automatic number plate recognition camera for vehicle speed measurement", European Conference on the Applications of Evolutionary Computation, Springer, pp. 803-818 (2016).
18. Luvizon, D.C., Nassu, B.T., and Minetto, R. "Vehicle speed estimation by license plate detection and tracking", 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE., pp. 6563-6567 (2014).
19. Sina, I., Wibisono, A., Nurhadiyatna, A., et al. "Vehicle counting and speed measurement using headlight detection", 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS), IEEE., pp. 149-154 (2013).
20. Famouri, M., Azimifar, Z., and Wong, A. "A novel motion plane-based approach to vehicle speed estimation", IEEE Transactions on Intelligent Transportation Systems, 20(4), pp. 1237-1246 (2018).
21. Khosravi, H. and Aghayan, S.A. "Real-time vehicle speed estimation from monocular camera using center of gravity tracking and implementation on the XU4 development board", 10th Iranian Conf. Machine Vision and Image Processing (in Farsi), Isfahan, Iran (2017).
22. Dubska, M., Herout, A., and Sochor, J. "Automatic camera calibration for traffic understanding", British Machine Vision Conference (BMVC), p. 8 (2014).
23. Khosravi, H. and Asgarian Dehkordi, R. "Automatic detection of vehicle dimensions using video received from roadside cameras", 10th Iranian Conf. Machine Vision and Image Processing (in Farsi), Isfahan, Iran (2017).
24. Lin, C.T., Yang, C.T., Shou, Y.W., et al. "An efficient and robust moving shadow removal algorithm and its applications in ITS", EURASIP Journal on Advances in Signal Processing 2010, pp. 1-19 (2010).
25. Wang, J., Zheng, H., Huang, Y., et al. "Vehicle type recognition in surveillance images from labeled web-nature data using deep transfer learning", IEEE Transactions on Intelligent Transportation Systems, 19(9), pp. 2913-2922 (2017).
26. Krizhevsky, A., Sutskever, I., and Hinton, G.E. "Imagenet classification with deep convolutional neural networks", Advances in Neural Information Processing Systems, pp. 1097-1105 (2012).
27. Ren, S., He, K., Girshick, R., et al. "Faster rcnn: Towards real-time object detection with region proposal networks", Advances in Neural Information Processing Systems, pp. 91-99 (2015).
28. Yang, L., Luo, P., Change Loy, C., et al. "A largescale car dataset for fine-grained categorization and verification", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3973- 3981 (2015).
29. Yu, S., Wu, Y., Li, W., et al. "A model for  inegrained vehicle classification based on deep learning", Neurocomputing, 257, pp. 97-103 (2017).
30. Gholamalinezhad, H. and Khosravi, H. "IRVD: A large-scale dataset for classification of Iranian vehicles in urban streets", Journal of AI and Data Mining, 9(1), pp. 1-9 (2020).
31. Hsieh, J.W., Chen, L.C., and Chen, D.Y. "Symmetrical SURF and its applications to vehicle detection and vehicle make and model recognition", IEEE Transactions on Intelligent Transportation Systems, 15(1), pp. 6-20 (2014).
32. Daniilidis, K., How to Compute Intrinsics from Vanishing Points, cited 2020. Available from: https:// www.coursera.org/lecture/robotics-perception/how-to -compute-intrinsics-from-vanishing-points-jnaLs (2018).
33. Zivkovic, Z. "Improved adaptive Gaussian mixture model for background subtraction", Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004, IEEE, pp. 28-31 (2004).
34. Kamijo, S., Matsushita, Y., Ikeuchi, K., et al. "Traffic monitoring and accident detection at intersections", IEEE Transactions on Intelligent Transportation Systems, 1(2), pp. 108-118 (2000).
35. Eraqi, H., Bag of Visual Words for Image Classification, cited 2020. Available from: http://heraqi. blogspot.com/2017/03/BoW.html (2017).
Scientia Iranica
Volume 29, Issue 5
Transactions on Computer Science & Engineering and Electrical Engineering (D)
September and October 2022
Pages 2515-2525

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