An Advanced Stereo Vision Based Obstacle Detection with a Robust Shadow Removal Technique
This paper presents a robust method to detect obstacles in stereo images using shadow removal technique and color information. Stereo vision based obstacle detection is an algorithm that aims to detect and compute obstacle depth using stereo matching and disparity map. The proposed advanced method is divided into three phases, the first phase is detecting obstacles and removing shadows, the second one is matching and the last phase is depth computing. We propose a robust method for detecting obstacles in stereo images using a shadow removal technique based on color information in HIS space, at the first phase. In this paper we use Normalized Cross Correlation (NCC) function matching with a 5 × 5 window and prepare an empty matching table τ and start growing disparity components by drawing a seed s from S which is computed using canny edge detector, and adding it to τ. In this way we achieve higher performance than the previous works [2,17]. A fast stereo matching algorithm is proposed that visits only a small fraction of disparity space in order to find a semi-dense disparity map. It works by growing from a small set of correspondence seeds. The obstacle identified in phase one which appears in the disparity map of phase two enters to the third phase of depth computing. Finally, experimental results are presented to show the effectiveness of the proposed method.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1333724Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1708
 I. Ulrich, I. Nourbakhsh, "Appearance-Based Obstacle Detection with Monocular Color Vision", AAAI National Conference on Artificial Intelligence, Austin, TX, July/August 2000.
 S. Fazli, H. Mohammadi Dehnavi, and P. Moallem, "A Robust Obstacle Detection Method in Highly Textured Environments using Stereo Vision", 2nd international conference on machine vision (IEEE), p 97-100, 2009.
 P. Munro, A. P. Gerdelan, "Stereo Vision Computer Depth Perception", Country United States City University Park Country Code US Post code 16802 Number of Publications 1,358,061 Number of Authors 1,096,162 Date of last added publication 2009.
 P. Foggia, Jean-Michel Jolion, A. Limongiello, and M. Vento, "Stereo Vision for Obstacle Detection: a Graph-Based Approach", 6th IAPR - TC-15 Workshop on Graph-based Representations in Pattern Recognition (GbR 2007), Alicante June 11-13, 2007.
 P. H. Batavia, S. Singh, "Obstacle Detection Using Adaptive Color Segmentation and Color Stereo Homography", IEEE International Conference on Robotics and Automation, May, 2001.
 Y. Chao, Z. Changan, "Obstacle Detection Using Adaptive Color Segmentation and Planar Projection Stereopsis for Mobile Robots", IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, October 2003.
 J. Coughlan, H. Shen, "TERRAIN ANALYSIS FOR BLIND WHEELCHAIR USERS: COMPUTER VISION ALGORITHMS FOR FINDING CURBS AND OTHER NEGATIVE OBSTACLES", Conference & Workshop on Assistive Technologies for People with Vision & Hearing Impairments Assistive Technology for All Ages CVHI 2007.
 P., Bellutta, R., Manduchi, L., Matthies, K. Owens, and A. Rankin, Terrain Perception for DEMO III. Intelligent Vehicle Symposium 2000.
 D. Sharstein, R. Szeliski, and R. Zabih. A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV, 47(1):7- 42, 2002.
 P, Moallem and K, Faez, "Effective Parameters in Search Space Reduction Used in a Fast Edge-Based Stereo Matching", Journal of Circuits, Systems, and Computers, 14(2): 249-266, 2005.
 S. B. Pollard, J. E. W. Mayhew, and J. P. Frisby. PMF: A stereo correspondence algorithm using a disparity gradient constraint. Perception, 14:449-470, 1985.
 R. M. Haralick and L. G. Shapiro. Image segmentation techniques. CVGIP, 29:100-132, 1985.
 Z. Megyesi, G. K'os, and D. Chetverikov. Dense 3D reconstruction from images by normal aided matching. Machine Graphics and Vision, 15:3-28, 2006.
 G. P. Otto and T. K. W. Chau. ÔÇÿRegion-growing- algorithm for matching of terrain images. IVC, 7(2):83-94, 1989.
 M. A. O-Neill and M. I. Denos. Practical approach to the stereo matching of urban imagery. IVC, 10(2):89-98, 1992.
 T. Kim and J. Muller. Automated urban area building extraction from high resolution stereo imagery. IVC, 14:115-130, 1996.
 Jan Cech, Radim Sara. Efficient Sampling of Disparity Space for Fast and Accurate Matching. In Proc. BenCOS Workshop CVPR, 2007.
 Radim Sara. Finding the largest unambiguous component of stereo matching. In Proc. ECCV, pp. 900-14, 2002.
 Radim Sara. Robust Correspondence Recognition for Computer Vision. In Proc COMPSTAT, 17th Conference of IASC-ERS Roma, Italy. 2006.
 R. Sara. Finding the largest unambiguous component of stereo matching. In Proc ECCV, pp. 900-914, 2002.
 R. Sara. Robust correspondence recognition for computer vision. In Proc COMPSTAT, pp. 119-131. Physica-Verlag, 2006.
 D. Gusfield and R.W. Irving. The Stable Marriage Problem: Structure and Algorithms. The MIT Press, 1989.