Open Access

StereoBox: A Robust and Efficient Solution for Automotive Short-Range Obstacle Detection

EURASIP Journal on Embedded Systems20072007:070256

Received: 30 October 2006

Accepted: 15 April 2007

Published: 8 July 2007


This paper presents a robust method for close-range obstacle detection with arbitrarily aligned stereo cameras. System calibration is performed by means of a dense grid to remove perspective and lens distortion after a direct mapping between image pixels and world points. Obstacle detection is based on the differences between left and right images after transformation phase and with a polar histogram, it is possible to detect vertical structures and to reject noise and small objects. Found objects' world coordinates are transmitted via CAN bus; the driver can also be warned through an audio interface. The proposed algorithm can be useful in different automotive applications, requiring real-time segmentation without any assumption on background. Experimental results proved the system to be robust in several envitonmental conditions. In particular, the system has been tested to investigate presence of obstacles in blind spot areas around heavy goods vehicles (HGVs) and has been mounted on three different prototypes at different heights.

[1 2 3 4 5 6 7 8 9]

Authors’ Affiliations

VisLab, Dipartimento Ingegreria Informazione, Università di Parma


  1. Broggi A, Caraffi C, Fedriga RI, Grisleri P: Obstacle detection with stereo vision for off-road vehicle navigation. Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), June 2005, San Diego, Calif, USA 65.Google Scholar
  2. Bertozzi M, Broggi A, Fascioli A: Stereo inverse perspective mapping: theory and applications. Image and Vision Computing 1998,16(8):585-590. 10.1016/S0262-8856(97)00093-0View ArticleGoogle Scholar
  3. Labayrade R, Aubert D, Tarel J-P: Real time obstacle detection on non flat road geometry through "v-disparity" representation. Proceedings of IEEE Intelligent Vehicles Symposium, June 2002, Versailles, France 2: 646-651.Google Scholar
  4. Claus D, Fitzgibbon AW: A rational function lens distortion model for general cameras. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), June 2005, San Diego, Calif, USA 1: 213-219.Google Scholar
  5. Devernay F, Faugeras O: Straight lines have to be straight. Machine Vision and Applications 2001,13(1):14-24. 10.1007/PL00013269View ArticleGoogle Scholar
  6. Tsai R: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE Journal of Robotics and Automation 1987,3(4):323-344.View ArticleGoogle Scholar
  7. Bertozzi M, Broggi A, Medici P, Porta PP, Sjögren A: Stereo vision-based start-inhibit for heavy goods vehicles. Proceedings of IEEE Intelligent Vehicles Symposium (IVS '06), June 2006, Tokyo, Japan 350-355.Google Scholar
  8. Bertozzi M, Broggi A: GOLD: a parallel real-time stereo vision system for generic obstacle and lane detection. IEEE Transactions on Image Processing 1998,7(1):62-81. 10.1109/83.650851View ArticleGoogle Scholar
  9. Lee K, Lee J: Generic obstacle detection on roads by dynamic programming for remapped stereo images to an overhead view. Proceedings of IEEE International Conference on Networking, Sensing and Control (ICNSC '04), March 2004, Taipei, Taiwan 2: 897-902.Google Scholar


© Broggi et al. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.