Ali Tourani; Asadollah Shahbahrami; Alireza Akoushideh; Saeed Khazaee; Ching. Y Suen

Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems

Full Text (PDF, 1128KB), PP.42-54

Views: 0 Downloads: 0

Author(s)

Ali Tourani ^1,* Asadollah Shahbahrami ¹ Alireza Akoushideh ² Saeed Khazaee ³ Ching. Y Suen ³

1. Department of Computer Engineering, University of Guilan, Rasht, Iran

2. Shahid-Chamran College, Technical and Vocational University, Tehran, Iran

3. Centre for Pattern Recognition and Machine Intelligence, Concordia University Montreal, Canada

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2019.04.04

Received: 24 Jan. 2019 / Revised: 15 Feb. 2019 / Accepted: 21 Mar. 2019 / Published: 8 Apr. 2019

Index Terms

Speed Measurement, Object Tracking, Video Processing, Intelligent Transportation Systems

Abstract

Video-based vehicle speed measurement systems are known as effective applications for Intelligent Transportation Systems (ITS) due to their great development capabilities and low costs. These systems utilize camera outputs to apply video processing techniques and extract the desired information. This paper presents a new vehicle speed measurement approach based on motion detection. Contrary to feature-based methods that need visual features of the vehicles like license-plate or windshield, the proposed method is able to estimate vehicle’s speed by analyzing its motion parameters inside a pre-defined Region of Interest (ROI) with specified dimensions. This capability provides real-time computing and performs better than feature-based approaches. The proposed method consists of three primary modules including vehicle detection, tracking, and speed measurement. Each moving object is detected as it enters the ROI by the means of Mixture-of-Gaussian background subtraction method. Then by applying morphology transforms, the distinct parts of these objects turn into unified filled shapes and some defined filtration functions leave behind only the objects with the highest possibility of being a vehicle. Detected vehicles are then tracked using blob tracking algorithm and their displacement among sequential frames are calculated for final speed measurement module. The outputs of the system include the vehicle’s image, its corresponding speed, and detection time. Experimental results show that the proposed approach has an acceptable accuracy in comparison with current speed measurement systems.

Cite This Paper

Ali Tourani, Asadollah Shahbahrami, Alireza Akoushideh, Saeed Khazaee, Ching. Y Suen, " Motion-based Vehicle Speed Measurement for Intelligent Transportation Systems", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.4, pp. 42-54, 2019. DOI: 10.5815/ijigsp.2019.04.04

Reference

[1]S.A. Shaheen and R. Finson, “Intelligent Transportation Systems,” Reference Module in Earth Systems and Environmental Sciences, Elsevier Encyclopedia of Energy, 2013.

[2]A. Auer, S. Feese and S. Lockwood, “History of Intelligent Transportation Systems”, U.S. Department of Transportation, Report No. FHWA-JPO-16-329, 2016. See https://www.its.dot.gov/index.htm. Retrieved 7 June 2018.

[3]G. Aifadopoulou, E. Mitsakis and P. Iordanopoulos, “Intelligent Transportation Systems in South-East Europe,” South East Europe Intelligent Transportation Systems (SEE-ITS) Project Report, 2014.

[4]F. Zhu, Z. Li, S. Chen and G. Xiong, “Parallel Transportation Management and Control System and its Applications in Building Smart Cities,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 6, pp. 1576-1585, 2016.

[5]F.Y. Wang, “Parallel Control and Management for Intelligent Transportation Systems: Concepts, Architectures, and Applications,” IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 3, pp. 630-638, 2010.

[6]V. Kastrinaki, M. Zervakis and K. Kalaitzakis, “A Survey of Video Processing Techniques for Trafﬁc Applications,” Image and Vision Computing, vol. 21, pp. 359-381, 2003.

[7]J. Zhang, F.Y. Wang, K. Wang, W.H. Lin, X. Xu and C. Chen, “Data-Driven Intelligent Transportation Systems: A Survey,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 4, pp. 1624-1639, 2011.

[8]M. Bommes, A. Fazekas, T. Volkenhoff and M. Oeser, “Video Based Intelligent Transportation Systems – State of the Art and Future Development,” Transportation Research Procedia, vol. 14, pp. 4495-4504, 2016.

[9]S. Lefèvre, C. Sun, R. Bajcsy and C. Laugier, “Comparison of Parametric and Non-Parametric Approaches for Vehicle Speed Prediction,” American Control Conference, Portland, pp. 3494-3499, 2014.

[10]M.A. Adnan, N. Sulaiman, N.I. Zainuddin and T.B.H.T. Besar, “Vehicle Speed Measurement Technique using Various Speed Detection Instrumentation,” IEEE Business Engineering and Industrial Applications Colloquium, Langkawi, pp. 668-672, 2013.

[11]P. Pérez, C. Hue, J. Vermaak and M. Gangnet, “Color-based Probabilistic Tracking,” 7th European Conference on Computer Vision, London, pp. 661-675, 2002.

[12]Amirkabir Artificial Intelligence Competitions Urban Highway Traffic dataset (AAIC_UHT). See link: http://www.aaic.ir. Retrieved 11 March 2018.

[13]D. Doria and R.J. Radke, “Filling Large Holes in LiDAR Data by Inpainting Depth Gradients,” IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, Providence, pp. 65-72, 2012.

[14]Pierre Soille, “Morphological Image Analysis: Principles and Applications,” Springer Science & Business Media, ISBN: 3662039397, 2013.

[15]L. Xu, J. Jia and Y. Matsushita, “Motion Detail Preserving Optical Flow Estimation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 9, pp. 1744-1757, 2012.

[16]M. Goldhammer, S. Köhler, K. Doll and B. Sick, “Camera based Pedestrian Path Prediction by Means of Polynomial Least-Squares Approximation and Multilayer Perceptron Neural Networks,” 3rd Science and Information Conference, London, pp. 390-399, 2015.

[17]H. Eslami, A. Raie and K. Faez, “Precise Vehicle Speed Measurement for Law Enforcement Applications based on Calibrated Camera with Parallel Standard Patterns,” IET Computer Vision, vol. 10, no. 5, pp. 398 –406, 2016.

[18]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.

[19]A. Mukhtar, L. Xia and T. B. Tang, “Vehicle Detection Techniques for Collision Avoidance Systems: A Review,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2318-2338, 2015.

[20]R.A. Hadi, G. Sulong and L.E. George, “Vehicle Detection and Tracking Techniques: A Concise Review,” International Journal of Signal & Image Processing, vol. 5, no. 1, pp. 1-12, 2014.

[21]N. Buch, S.A. Velastin and J. Orwell, “A Review of Computer Vision Techniques for the Analysis of Urban Traffic,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 920-939, 2011.

[22]J. Sochor, “Fully Automated Real-Time Vehicles Detection and Tracking with Lanes Analysis,” 18th Central European Seminar on Computer Graphics, Smolenice, pp. 59-66, 2014.

[23]M. Nieto, L. Unzueta, J. Barandiaran, A. Cortés, O. Otaegui and P. Sanchez, “Vehicle Tracking and Classification in Challenging Scenarios via Slice Sampling,” The European Association for Signal Processing Journal on Advances in Signal Processing, vol. 11, no. 1, pp. 95-112, 2011.

[24]Y. Li, M.J. Er and D. Shen, “A Novel Approach for Vehicle Detection using an AND–OR-Graph-Based Multiscale Model,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 4, pp. 2284-2289, 2015.

[25]Y. Li, B. Li, B. Tian and Q. Yao, “Vehicle Detection based on the AND–OR Graph for Congested Traffic Conditions," IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 2, pp. 984-993, 2013.

[26]M. Liang, X. Huang, C.H. Chen, X. Chen and A. Tokuta, “Counting and Classification of Highway Vehicles by Regression Analysis,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2878-2888, 2015.

[27]B. Tian, Y. Li, B. Li and D. Wen, “Rear-View Vehicle Detection and Tracking by Combining Multiple Parts for Complex Urban Surveillance,” IEEE Transactions on Intelligent Transportation Systems, vol. 15, no. 2, pp. 597-606, 2014.

[28]B.F. Wu, C.C. Kao, C.L. Jen, Y.F. Li, Y.H. Chen and J.H. Juang, “A Relative-Discriminative-Histogram-of-Oriented-Gradients based Particle Filter Approach to Vehicle Occlusion Handling and Tracking,” IEEE Transactions on Industrial Electronics, vol. 61, no. 8, pp. 4228-4237, 2014.

[29]I. Sina, A. Wibisono, A. Nurhadiyatna, B. Hardjono, W. Jatmiko and P. Mursanto, “Vehicle Counting and Speed Measurement using Headlight Detection,” International Conference on Advanced Computer Science and Information Systems, Bali, pp. 149-154, 2013.

[30]G. Garibotto, P. Castello, E. Del Ninno, P. Pedrazzi and G. Zan, “Speed-Vision: Speed Measurement by License Plate Reading and Tracking,” IEEE Intelligent Transportation Systems Conference, Oakland, pp. 585-590, 2001.

[31]B. Alefs and D. Schreiber, “Accurate Speed Measurement from Vehicle Trajectories using AdaBoost Detection and Robust Template Tracking,” IEEE Intelligent Transportation Systems Conference, Seattle, pp. 405-412, 2007.

[32]D.C. Luvizon, B.T. Nassu and R. Minetto, “A Video-Based System for Vehicle Speed Measurement in Urban Roadways,” IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 6, pp. 1393-1404, 2017.

[33]D.F. Llorca, C. Salinas, M. Jimenez, I. Parra, A.G. Morcillo, R. Izquierdo, J. Lorenzo and M.A. Sotelo, “Two-camera based Accurate Vehicle Speed Measurement using Average Speed at a Fixed Point,” 19th IEEE International Conference on Intelligent Transportation Systems, Rio de Janeiro, pp. 2533-2538, 2016.

[34]J. Lan, J. Li, G. Hu, B. Ran and L. Wang, “Vehicle Speed Measurement based on Gray Constraint Optical Flow Algorithm,” International Journal for Light and Electron Optics, vol. 125, no. 1, pp. 289-295, 2014.

[35]M. Piccardi, “Background Subtraction Techniques: A Review,” IEEE International Conference on Systems, Man and Cybernetics, vol.4, pp. 3099-3104, 2004.

[36]B. Tian, Q. Yao, Y. Gu, K. Wang and Y. Li, “Video Processing Techniques for Trafﬁc Flow Monitoring: A Survey,” 14th IEEE International Conference on Intelligent Transportation Systems, Washington DC, pp. 1103-1108, 2011.

[37]P. KaewTraKulPong and R. Bowden, “An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection,” 2nd European Workshop on Advanced Video-based Surveillance Systems, Genova, 2002.

[38]A. Nurhadiyatna, B. Hardjono, A. Wibisono, W. Jatmiko and P. Mursanto, "ITS Information Source: Vehicle Speed Measurement Using Camera as Sensor," International Conference on Advanced Computer Science and Information Systems, Depok, pp. 179-184, 2012.

[39]University of Guilan Business Incubator Website: http://roshd.arshiaweb.ir/. Retrieved 5 July 2018.

International Journal of Image, Graphics and Signal Processing (IJIGSP)