Real-Time Obstacle Detection Approach using Stereoscopic Images

Full Text (PDF, 789KB), PP.42-48

Views: 0 Downloads: 0


Nadia Baha 1,*

1. Computer Science Department, University of Science and Technology Houari Boumediene, Algiers, Algeria

* Corresponding author.


Received: 5 Nov. 2013 / Revised: 7 Dec. 2013 / Accepted: 9 Jan. 2014 / Published: 8 Feb. 2014

Index Terms

Obstacle detection, real-time, stereovision, disparity map


In this paper, we propose a new and simple approach to obstacle and free space detection in an indoor and outdoor environment in real-time using stereo vision as sensor. The real-time obstacle detection algorithm uses two dimensional disparity map to detect obstacles in the scene without constructing the ground plane. The proposed approach combines an accumulating and thresholding techniques to detect and cluster obstacle pixels into objects using a dense disparity map. The results from both analysis modules are combined to provide information of the free space. Experimental results are presented to show the effectiveness of the proposed method in real-time.

Cite This Paper

Nadia Baha, "Real-Time Obstacle Detection Approach using Stereoscopic Images", International Journal of Information Engineering and Electronic Business(IJIEEB), vol.6, no.1, pp.42-48, 2014. DOI:10.5815/ijieeb.2014.01.05


[1]Mittal A., Sofat S. and Venkatesh M. A Novel Color Coherence Vector based Obstacle Detection Algorithm for Textured Environments. The 3rd International Conference on Machine Vision ICMV2010, 2010.

[2]Geronimo D., Lopez A. M., Sappa A.D., Graf T., Survey of Pedestrian Detection for Advanced Driver Assistance Systems, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, issue 7, 2010, 1239 - 1258.

[3]Sun Z., Bebis G., Miller R., On-road vehicle detection: a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 5, May 2006, 694 - 711.

[4]Cho H., Rybski P.E, ZhangW., Vision-based bicycle detection and tracking using a deformable part model and an EKF algorithm, 2010 13th International IEEE Conference on Intelligent Transportation Systems (ITSC), 2010, 1875 - 1880.

[5]Kang J. ,Chung M.J Stereo-Vision Based Free Space and Obstacle Detection with Structural and Traversability analysis Using Probabilistic Volume Polar Grid Map, IEEE 5th International Conference on Robotics, Automation and Mechatronics (RAM) 2011. 

[6]Nedevschi S., Schmidt R., Graf T.and Danescu R. High Accuracy Stereo Vision System for Far Distance obstacle Detection. IEEE Intelligent Vehicles Symposium, Parma, Italy, June 2004. 

[7]Chan Nguyen Viet, An efficient obstacle detection algorithm using color and texture. World Academy of Science and Technology, 2009. 

[8]Suard F. A Pedestrian detection using infrared images ad histograms of oriented gradients.Proceedings of the IEEE Intelligent Vehicles Symposium,Tokyo, Japan, june 2006.

[9]Ulrich I. , Nourbakhsh I.Appearance-Based Obstacle Detection with Monocular Color Vision. Proc. of the AAAI National Conference on Artificial Intelligence, Austin, TX, July/August 2000.

[10]Chumerin N., Ground plane estimation based on dense stereo disparity, Int. Conf. on neural networks and artificial intelligence,2008.

[11]Nguyen Viet C.,An efficient obstacle detection algorithm using color and texture, World Academy of Science and Technology, 2009.

[12]Nguyen T. Real-time obstacle detection for an Autonomous wheelchair using stereoscopic cameras" In Proc. Of the 29th Annual Inter. Conf. of the IEEE EMBS, August 2007, 4775-4778.

[13]Baha N., Larabi S. Obstacle Detection With Stereo Vision based on the homography.In proc. Acit 2013,2007, 65-71.

[14]Aldavert D. Obstacle Detection and alignment using an stereo camera pair. Technical report, Artificial Intelligence research institute, Spain, 2008.

[15]Chumerin N. Ground plane estimation based on dense stereo disparity. Int. Conf. on neural networks and artificial intelligence, 2008.

[16]Labayrade R, Aubert D. In vehicle obstacle detection and characterization by stereovision. In IEEE Intelligent Vehicles Symposium, Versailles.2004.

[17]Lorigo L.M, Brooks R.A. Grimson W.E.L, Visually guided obstacle avoidance in unstructured Environnement In IROS, 1997, 373-379.

[18]Burschka D., Hager G.Stage classification from dense disparity maps in indoor environments. In Proc of ICPR Quebec, August 2002.

[19]Zang Z., Weiss R., Hansoon A.R.Obstacle detection based one qualitative and Quantitative 3D reconstruction. IEEE Tran on pattern analysis and machine Intelligence, Janv 1997, 15-26.

[20]Okada R.,.Onouchi K. Obstacle detection using projective invariant and vanishing lines. IEEE Int Conf on Computer Vision France Octobre 2003.

[21]Aldavert D. Obstacle Detection and alignment using an stereo camera pairs pair. Technical report, Artificial Intelligence research institute, Spain, 2008.

[22]Kubota S., Nakano T., Okamoto Y. A Global Optimization Algorithm for Real-Time On-Board Stereo Obstacle Detection Systems. IEEE Intelligent Vehicles Symposium, 2007, 7 - 12.

[23]Xu Y., Zhao M., Wang X., Zhang Y., Peng Z., YuanY., Liu H. .A method of stereo obstacle detection based on image symmetrical move, 2009 IEEE Intelligent Vehicles Symposium, 2009, 36 - 41.

[24]Yang M., Yu Q., Wang H. Zhang B..Vision-based real-time obstacle detection and tracking for autonomous vehicle guidance. Proceedings Vol. 4666, Real-Time Imaging VI, February 2002, 65-74.

[25]Perrollaz M., Spalanzani A., and Aubert D.A probabilistic representation of the uncertainty of stereo-vision and its application to obstacle, IEEE Intelligent Vehicles Symposium (IV), June 2010, 313-318.

[26]Manduchi R., A. Castano A., Talukder A. and Matthies L.Obstacle and terrain classification for autonomous off-road navigation, Autonomous Robots, 2005, vol. 18, no. 1, 81-102.

[27]Vergauwen M., Pollefeys M., and Gool L. V.A stereo-vision system for support of planetary surface exploration, Machine Vision and Applications, April 2003, vol. 14, no. 1. 5-14.

[28]Kayama K., Yairi I.E., Igi S..Construction of elevation map for usercarried outdoor mobile robot using stereo vision, IEEE International Conference on Systems, Man and Cybernetics, 2003, 4631 - 4636.

[29]Oniga F., Nedevschi S..Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection," IEEE Transactions on Vehicular Technology, March 2010, vol. 59, issue 3, 1172 - 1182.




[33]  =isch&ei=CebQT5W0DpCM4gT7kezRDA&sa=X&oi=mode_link&ct=mode&cd=2&ved=0CEIQ_ AUoAQ&biw=1280&bih=705.

[34]Lefebvre S. Mise en correspondance stéréoscopique de fenetres monodimenensionnelles par logique floue: application à la détection d'obstacles routiers. Thesis in INRETS-LEOST, France, July 2008.