Efficient disparity calculation based on stereo vision with ground obstacle assumption

This paper presents a fast local disparity calculation algorithm on calibrated stereo images for automotive applications. By utilizing the ground obstacle assumption for a typical road scene, only a small fraction of disparity space is required to be visited in order to find a disparity map. It works by using the neighbourhood disparities of the pixels in the lower image line as supporting points to determine the search range of its upper vicinity line. Unlike the conventional seed growing based algorithms that are only capable of producing a semi-dense disparity map, the proposed algorithm utilises information provided by each pixel rather than trusting only the featured seeds. Hence, it is capable of providing a denser disparity output with low errors in homogeneous areas. The experimental results are also compared to a normal exhaustive search (block matching) algorithm, showing a factor of ten improvement in speed, whilst the accuracy is enhanced by 20% without constraint to the maximum possible disparity.

[1]  Antonios Gasteratos,et al.  Review of Stereo Vision Algorithms: From Software to Hardware , 2008 .

[2]  Sungchul Kang,et al.  Fast correlation-based stereo matching with the reduction of systematic errors , 2005, Pattern Recognit. Lett..

[3]  Long Quan,et al.  Match Propagation for Image-Based Modeling and Rendering , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Daniel P. Huttenlocher,et al.  Efficient Belief Propagation for Early Vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[5]  Andreas Geiger,et al.  Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[6]  D. Scharstein,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, Proceedings IEEE Workshop on Stereo and Multi-Baseline Vision (SMBV 2001).

[7]  Luigi di Stefano,et al.  A fast area-based stereo matching algorithm , 2004, Image Vis. Comput..

[8]  L. Hong,et al.  Segment-based stereo matching using graph cuts , 2004, CVPR 2004.

[9]  Radim Sára,et al.  Efficient Sampling of Disparity Space for Fast And Accurate Matching , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Sang Uk Lee,et al.  A dense stereo matching using two-pass dynamic programming with generalized ground control points , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[11]  Carsten Rother,et al.  Fast cost-volume filtering for visual correspondence and beyond , 2011, CVPR 2011.

[12]  Stefano Mattoccia,et al.  Linear stereo matching , 2011, 2011 International Conference on Computer Vision.

[13]  Li Hong,et al.  Segment-based stereo matching using graph cuts , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[14]  Naim Dahnoun,et al.  Local stereo disparity estimation with novel cost aggregation for sub-pixel accuracy improvement in automotive applications , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[15]  F FelzenszwalbPedro,et al.  Efficient Belief Propagation for Early Vision , 2006 .

[16]  Zhen Zhang,et al.  Real-time obstacle detection based on stereo vision for automotive applications , 2012, 2012 5th European DSP Education and Research Conference (EDERC).