Open Access Open Access  Restricted Access Subscription or Fee Access

A Proposed Monocular Vision Method for Automated Paved Shoulder Detection in Videos

Nícolas Pereira Borges(1), Nataniel Pereira Borges Jr(2*), Jorge Destri Jr(3), Camila Beleza Maciel Barreto(4), Valter Zanela Tani(5), Amir Mattar Valente(6)

(1) Computer Science Division, Aeronautics Institute of Technology, Brazil
(2) Department of Computer Science, Saarland University, Germany
(3) Transportation and Logistics Laboratory (LabTrans), Federal University of Santa Catarina, Brazil
(4) Transportation and Logistics Laboratory (LabTrans), Federal University of Santa Catarina, Brazil
(5) Transportation and Logistics Laboratory (LabTrans), Federal University of Santa Catarina, Brazil
(6) Department of Civil Engineering, Federal University of Santa Catarina, Brazil
(*) Corresponding author



The systematic recovery of design elements from the Brazilian federal highway network is vital for its efficient operation and maintenance. These information are necessary for studies that analyze the complete network or to prioritize interventions such as studies about on specific segments. This paper presents a computer vision algorithm for paved shoulder detection, using monocular cameras. Initially the lane and the possible shoulder are detected. Then the shoulder boundary is determined using length and homogeneity metrics. Finally, the shoulder on a frame is classified and, for error reduction, a sliding window is used to classify the shoulder in a point on the video. To evaluate the algorithm, videos of the federal highways BR-020 and BR-493 were used, achieving an accuracy of 89%.
Copyright © 2016 Praise Worthy Prize - All rights reserved.


Paved Shoulder Detection; Highway Feature Extraction; Computer Vision

Full Text:



TRB. Highway capacity manual. (Transportation Research Board, Washington D. C., 2010).

TRB. National Cooperative Highway Research Program - NCHRP - Report 633 - Impact of Shoulder Width and Median Width on Safety. Technical report (Transportation Research Board,Washington, D.C., 2009).

FHWA. Mitigation strategies for design exceptions. Technical report (Federal Highway Administration, Washington, D.C., 2007).

D. DeMenthon, L.S. Davis. Reconstruction of a Road by Local Image Matches and Global 3D Optimization, Robotics and Automation, 1990. Proceedings, 1990 IEEE International Conference on, Vol 3, pp. 1337–1342, Cincinnati, Ohio, May 1990.

R. Chapuis, R. Aufrere, F. Chausse. Accurate Road Following and Reconstruction by Computer Vision. Intelligent Transportation Systems, IEEE Transactions on, Vol. 3(Issue 4), pp.261–270, December 2002.

R. Wang, Y. Xu, Libin, Y. Zhao. A Vision-based Road Edge Detection Algorithm. Intelligent Vehicle Symposium, 2002. IEEE, Vol. 1, pp. 141–147, June 2002.

R. Risack, P. Klausmann, W. Krger, and W. Enkelmann. Robust Lane Recognition Embedded in a Realtime Driver Assistance System. IEEE International Conference on Intelligent Vehicles, Vol. 1, pp. 35–40, Piscataway, NJ, 1998.

D. A. Schwartz. Clothoid Road Geometry Unsuitable for Sensor Fusion Clothoid Parameter Sloshing. Intelligent Vehicles Symposium, 2003. Proceedings. IEEE, pp. 484–488, Piscataway, NJ, June 2003.

Y. Wang, D. Shen, E. K. Teoh. Lane Detection using Catmull-rom Spline. IEEE International Conference on Intelligent Vehicles, pp. 51–57, Stuttgart, Germany, October 1998.

A. A. M. Assidiq, O. O. Khalifa, R. Islam, S. Khan. Real time Lane Detection for autonomous vehicles. Computer and Communication Engineering, 2008. ICCCE 2008. International Conference on, pp. 82–88, Kuala Lumpur, Malaysia, May 2008.

L. A. Piegl, W. Tiller. Data approximation using biarcs. Engineering with Computers, Vol. 18(Issue 1) pp. 59–65, 2002.

C. Kreucher, S. Lakshmanan, K. Kluge. A Driver Warning System Based on the Lois Lane Detection Algorithm. Proceedings of IEEE International Conference on Intelligent Vehicles, pp. 17–22. Stuttgart, Germany, October 1998.

R. O’Malley, E. Jones, M. Glavin. Rear-lamp vehicle detection and tracking in low-exposure color video for night conditions. Intelligent Transportation Systems, IEEE Transactions on, 11(Issue 2), pp.453–462,June 2010.

I. M. Chira, A. Chibulcutean, R. G. Danescu. Real-time detection of road markings for driving assistance applications. Computer Engineering and Systems (ICCES), 2010 International Conference on, pp. 158–163, Cairo, Egypt, November 2010.

M. A. Garcia-Garrido, M. A. Sotelo, E. Martm-Gorostiza. Fast traffic sign detection and recognition under changing lighting conditions. Intelligent Transportation Systems Conference, 2006. ITSC ’06. IEEE, pp. 811–816, Toronto, Canada, September 2006.

F. Boi, L. Gagliardini. A support vector machines network for traffic sign recognition. Neural Networks (IJCNN), The 2011 International Joint Conference on, pp. 2210–2216, Eau Claire, WI, July 2011.

A. Broggi, C. Caraffi, R. I. Fedriga, P. Grisleri. Obstacle detection with stereo vision for off-road vehicle navigation. Computer Vision and Pattern Recognition - Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on, pp. 65–65, San Diego, CA, June 2005.

Y. Hong, W. Zhengyou, and H. Ruxia. Obstacle detection and road segmentation by 3-D reconstruction based on monocular vision. Journal of Chemical & Pharmaceutical Research, Vol. 6(Issue 7), pp. 1333–1340, 2014.

L. Huang. Roadside camera calibration and its application in length-based vehicle classification. Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on, Vol. 2, pp. 329–332, Wuhan, China, March 2010.

J. Roters, X. Jiang, K. Rothaus. Recognition of traffic lights in live video streams on mobile devices. Circuits and Systems for Video Technology, IEEE Transactions on, Vol. 21(Issue 10), pp. 1497–1511, October 2011.

Brasil. Ministério dos Transportes. Departamento Nacional de Infraestrutura de Transportes. Electronic Auction 057/2012-00 (Pregão Eletrônico 057/2012-00). Brasília, 2012 (in Portuguese).

Brasil. Ministério dos Transportes. Departamento Nacional de Infraestrutura de Transportes. Theme Management Report - 2012 Actions (Relatório de Gestão Temático – Ações de 2012). Brasília, 2012 (in Portuguese).

B. Benligiray, C. Topal, C. Akinlar. Video-based lane detection using a fast vanishing point estimation method. Multimedia (ISM), 2012 IEEE International Symposium on, pp. 348–351, Irvine, CA, December 2012.

J. Canny. A computational approach to edge detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, PAMI-Vol. 8(Issue 6), pp. 679–698, November 1986.

J. Matas, C. Galambos, J. Kittler. Robust detection of lines using the progressive probabilistic Hough transform. Computer Vision and Image Understanding, Vol. 78(Issue 1), pp. 119 – 137, 2000.

L. M. Vavra. Cramer’s rule. Master’s thesis, Department of Mathematics, University Warwick, England, 2010.

H. Jiawei, M. Kamber. Data mining: Concepts and Techniques, Vol. 5 (Morgan Kaufmann, San Francisco, CA, 2001).

Brasil. Ministério dos Transportes. Departamento Nacional de Estradas de Rodagem. Geometric Design Manual for rural roads (Manual de projeto geométrico de rodovias rurais), Rio de Janeiro, 1999 (in Portuguese).

R S. Gomide. Virtual Environment for upper limbs rehabilitation using computer vision (Ambiente virtual para reabilitção de membros superiores utilizando visao computacional). Master’s thesis, Federal University of Goiás, Goiania, 2012 (in Portuguese).

G. E. A. P. A. Batista, R. C. Prati, M. C. Monard. A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations Newsletter, Vol. 6(Issue 1), pp. 20–29, June 2004.

M. Junker, R. Hoch, A. Dengel. On the evaluation of document analysis components by recall, precision, and accuracy. Document Analysis and Recognition, 1999. ICDAR ’99. Proceedings of the Fifth International Conference on, pp. 713–716, Bangalore, India, September 1999.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2022 Praise Worthy Prize