Deep Learning Based Vehicle Speed Estimation on Highways
Main Article Content
Abstract
Traffic management always is the matter requiring the attention of highway system managers in terms of vehicle monitoring and speed estimation. This paper proposes an efficient deep learning-based vehicle speed estimation on highway lanes in the Vietnam transport system. The input videos are recorded by fixed surveillance cameras. An optimized single shot multibox detector network, called SSD is utilized for vehicle license plate detection (LPD). The deep SORT (simple online and real-time tracking) model is first applied to video vehicle tracking and performed in the detected license plate area. This tracking process investigates the traveling distance of a vehicle to estimate its speed. In this study, the dataset has been normalized to improve the efficiency of vehicle localization and tracking to improve the time elapsing in the estimation of the distance travelled by vehicles on highways. The results showed that the proposed system has achieved better accuracy in terms of the determined speeds with the errors ranging between [-1.5, +1.1] km/h, equivalent to 98% of the error limit by the regulation in Viet Nam.
Keywords
vehicle detection, speed estimation, feature extraction, LPD-SSD network, SORT model
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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[3] Nicolai Woke, Alex Bewley, Dietrich Paulus, Simple online and realtime tracking with a deep association metric, 2017 IEEE International Conference on Image Processing (ICIP), September 2017. https://doi.org/10.1109/ICIP.2017.8296962
[4] A. S. Gunavan, D. A. Tanjung, F. E. Gunavan, Detection of Vehicle Position and Speed using Camera Calibration and Image Projection Methods, 4th International Conference on Computer Science and Computational Intelligence 2019, pp. 255-265, September 2019.
[5] J. Sochor et. al, Comprehensive Dataset for Automatic Single Camera Visual Speed Measurement, IEEE transactions on intelligent transportation systems, pp(99) 1-11, May 2018.
[6] M. G. Moazzam, M. R. Haque, M. S. Udđin, ImageBased Vehicle Speed Estimation, Journal of Computer and Communications, Vol. 7, pp 1-5, 2019.
[7] A. Tourani et al, Motion-based Vehicle Speed Measurement for Intelligent Transportation System, Inter. Journal of Image, Graphics and Signal Processing, Vol. 4, pp. 42-55, 2019.
[8] Igor Ševo, Aleksej Avramovic, Convolutional neural network-based automatic object detection on aerial images, 2016 IEEE Geoscience and Remote Sensing Letters, 13(5):740-744, 2016. https://doi.org/10.1109/LGRS.2016.2542358
[9] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C. Berg, SSD: Single shot multibox detector, European Conference On Computer Vision, pp 21- 3729, Dec 2016, arXiv:1512.02325v5
[10] Alex Bewley, ZongYuan Ge, Lionel Ott, Fabio Ramos, Ben Upcroft, Simple online and realtime object tracking, 2016 IEEE International Conference on Image Processing (ICIP), September 2016. https://doi.org/10.1109/ICIP.2016.7533005
[11] Chris Stauffer, W.E.L. Grimson, Adaptive background mixture models for real-time tracking, 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 246-252, June 1999.
[12] Nguyen Viet Hung, Nguyen Thi Thao, Do Huy Khoi, Nguyen Tien Dung. Modeling method of vehicle speed detection based on image processing, Journal of Science and Technolgy, Thai Nguyen University, ISSN 1859 - 2171, Issue 169, 9/2017, pp 4-39
[13] David A. Forsyth, Jean Ponce, Computer Vision A Modern Approach, second edition, 2012.
[14] Massimo Piccardi, Background subtraction techniques: a review, IEEE International Conference on Systems, Man and Cybernetics, pp. 3099-3104, 2004.
[15] M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, The pascal visual object classes (voc) challenge, International Journal of Computer Vision, vol. 88, no. 2, pp. 303-338, 2010. https://doi.org/10.1007/s11263-009-0275-4