International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 5, No. 7, 8 Jun. 2013
Cover page and Table of Contents: PDF (size: 307KB)
An Analytical Review of Stereovision Techniques to Reconstruct 3D Coordinates
Full Text (PDF, 307KB), PP.80-86
Views: 0 Downloads: 0
Author(s)
Index Terms
Stereovision, 3D Imaging, Lane Detection, Particle Filtering, Obstacle Detection, Elevation Maps
Abstract
Stereovision based on 3D environment reconstruction provides a true picture of real world situations for detection of objects’ locations. This approach has specific use in the scenarios like identifying traffic jams on the roads, locating curves and bends on the roads, finding obstacles in the construction sites, etc. This paper describes different methods used in stereovision to detect images like use of trinocular stereovision, calculating correlation between left and right contours for achieving accuracy, use of prior information with intrinsic and extrinsic parameters, detection of side lane and 3D points of guardrails and fences, use of dense stereovision information, especially in urban environment. The paper also discusses Forward Collision Detection method that uses Elevation Map with Dense Stereovision, tracking of multiple objects using two-level approach and building an enhanced grid that involves obstacle cells. Hybrid dense stereo engine, which is used in urban detection scenarios is also discussed in the paper along with a solution of lane estimation in different situations using particle filtering method. Pattern matching using 3D image for pedestrian detection and lane estimation based on the particle filtering with greyscale images are also explored. The use of the rectangular digital elevation map for transforming stereo based information and the methodology used to enhance the sub pixel accuracy are also part of the paper.
Cite This Paper
Raheel Ahmed, Muhammad Naeem Ahmed Khan, "An Analytical Review of Stereovision Techniques to Reconstruct 3D Coordinates", International Journal of Information Technology and Computer Science(IJITCS), vol.5, no.7, pp.80-86, 2013. DOI:10.5815/ijitcs.2013.07.10
Reference
[1]S. Nedevschi, S. Bota, T. Marita, F. Oniga, C. Pocol. “Real-Time 3D Environment Reconstruction Using High Precision Trinocular Stereovision.” Automation, Quality and Testing, Robotics, 2006 IEEE International Conference on 25-28 May 2006.
[2]S. Nedevschi, F. Oniga, R. Danescu. “Increased Accuracy Stereo Approach for 3D Lane Detection.” Intelligent Vehicles Symposium 2006, June 13-15, 2006, Tokyo, Japan.
[3]S. Nedevschi, C. Vancea, T. Marita, T. Graf. “On-Line Calibration Method for Stereovision systems Used in Vehicle Applications.” Intelligent Transportation Systems Conference, 2006. ITSC '06. IEEE Dated: 17-20 Sept. 2006.
[4]R. Danescu, S. Sobol, S. Nedevschi, T. Graf. “Stereovision-Based Side Lane and Guardrail Detection”. Intelligent Transportation Systems Conference, 2006. ITSC '06.IEEE Dated: 17-20 Sept. 2006.
[5]S. Nedevschi, R. Danescu, C. Pocol. M.M. Meinecke. “Stereo Image Processing for ADAS and Pre-Crash Systems”. 5th International Workshop on Intelligent Transportation -- WIT 2008.”. Dated: 19 March 2008.
[6]S. Nedevschi, A. Vatavu, F. Oniga. “Forward Collision Detection based on Elevation Map from Dense Stereo”. Intelligent Computer Communication and Processing, 2008. ICCP 2008. 4th International Conference on Dated: 28-30 Aug. 2008
[7]R. Danescu, F. Oniga, S. Nedevschi, M.M. Meinecke. “Tracking Multiple Objects Using Particle Filters and Digital Elevation Maps”. Intelligent Vehicles Symposium, 2009, IEEE Dated: 3-5 June 2009, Digital Object Identifier : 10.1109/IVS.2009.5164258
[8]F. Oniga, S. Nedevschi, M.M. Meinecke. “Temporal Integration of Occupancy Grids Detected from Dense Stereo Using an Elevation Map Representation”. 6th International Workshop on Intelligent Transportation (WIT 2009), Hamburg, Germany, March 24-25, 2009, pp. 133-138.
[9]C. Pocol, S. Nedevschi, M.M. Meinecke “Obstacle Detection Based on Dense Stereovision for Urban ACC Systems”. WIT 2008: 5th International Workshop on Intelligent Transportation, 18-19 March 2008, Hamburg, Germany.
[10]S. Nedevschi, Radu Danescu, T, Marita, F. Oniga, C. Pocol, S. Sobol, C. Tomiuc, C. Vancea, M.M. Meinecke, T. Graf, T. Binh To, M.A. Obojski “A Sensor for Urban Driving Assistance Systems Based on Dense Stereovision”. Intelligent Vehicles Symposium, 2007 IEEE Dated: 13-15 June 2007.
[11]Radu Danescu, Sergiu Nedevschi “Problilistic Lane Tracking in Difficult Road Scenarios Using Stereovision”. IEEE Transactions on Intelligent Transportation Systems, Vol 10, No. 2 June 2009.
[12]S. Nedevschi, S. Bota, C. Tomiuc “Stereo-Based Pedestrian Detection for Collision-Avoidance Applications”. IEEE Transactions on Intelligent Transportation Systems, Vol 10, No. 3 September 2009.
[13]R. Danescu, S. Nedevschi, M.M. Meinecke, T. Binh To “A Stereovision-Based Probabilistic Lane Tracker for Difficult Road Scenarios”. Intelligent Vehicles Symposium, 2008 IEEE Dated: 4-6 June 2008.
[14]Florin Oniga , Sergiu Nedevschi “Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle and Obstacle Detection”. Vehicular Technology, IEEE Transactions on Dated: March 2010.
[15]Istvan Haller, Sergiu Nedevschi “Design of Interpolation Functions for Sub-Pixel Accuracy Stereo-Vision Systems”. IEEE Transactions on Image Process. 2011 Jul 29. DOI: 10.1109/TIP.2011.2163163.
[16]Zhibin Zhang, Shuangshuang Liu , Gang Xu , Juangjuang Wang, “A Vehicle Distance Measurement Based On Binocular Stereo Vision”. Journal of Theoretical and Applied Information Technology 31st October 2012. Vol. 44 No.2.
[17]T. Surgailis, A. Valinevicius, V. Markevicius, D. Navikas, D. Andriukaitis, “Avoiding Forward Car Collision using Stereo Vision System”. Elektronika ir elektrotechnika, issn 1392-1215, vol. 18, no. 8, 2012.
[18]Sayanan Sivaraman, Brendan Morris, and Mohan Trivedi, “Learning Multi-Lane Trajectories using Vehicle-Based Vision”. 2011 IEEE International Conference on Computer Vision Workshops 978-1-4673-0063-6/11.
[19]E. Trucco, A. Verri, “Introductory Techniques for 3D Computer Vision.” Prentice Hall, 1998.
[20]T. A. Williamson,”A High-Performance Stereo Vision System for Obstacle Detection”, technical report CMU-RI-TR-98-24, Robotics Institute, Carnegie Mellon University, September 1998.
[21]R. Y. Tsai. “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, RA-3(4)/1987: 99.323.344. 1987.
[22]Z. Zhang. “Flexible Camera Calibration by Viewing a Plane From Unknown Orientations.”International Conference on Computer Vision (ICCV’99), Corfu, Greece, September 1999, pp. 666-673. 1999.
[23]J. Heikkila, O. Silven, “A four-step camera calibration procedure with implicit image correction,” Proc. ICCC Computer Society Conf, 1997. 1106-1112. 1997.
[24]J. Y. Bouguet. Camera Calibration Toolbox for Matlab, http://www.vision.caltech.edu/bouguetj/calib_doc
[25]S. Nedevschi, R. Danescu, D. Fretiu, T. Marita, F. Oniga, C. Pocol, R. Schmidt, T. Graf, “ High Accuracy Stereo Vision System for Far Distance Obstacle Detection”, IEEE Intelligent Vehicles Symposium (IV 2004), Parma Italy, pp. 292-297, 2004.
[26]S. Nedevschi, R. Schmidt, T. Graf, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, “3D Lane Detection System Based on Stereovision”, IEEE Intelligent Transportation Systems Conference (ITSC), Washington, USA. Pp 161-166, 2004.
[27]J. Canny, “A Computational Approach to Edge Detection.” IEEE Trans Pattern Analysis and Machine Intelligence. Vol. 8 No. 6 June 1986. Pp. 679-698.
[28]Image Obtained from the website: http://users.fmrib.ox.ac.uk/~steve/susan/susan/img90.gif
[29]Image Obtained from the website:http://ecx.images-amazon.com/images/I/51NslhcP0PL. _SL500_AA300_.jpg