한국자동차공학회논문집 (Transactions of the Korean Society of Automotive Engineers)

  • 제23권6호
  • /
  • Pages.565-574
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  • 2015
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  • 1225-6382(pISSN)
  • /
  • 2234-0149(eISSN)
한국자동차공학회 (The Korean Society of Automotive Engineers)
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영상 영역 특징 추가 및 유전 알고리즘 기반 최적화를 통한 스틱셀 분할 개선 방법

Improvement of Stixel Segmentation Using Additive Image Domain Features and Genetic Algorithm-based Optimization

  • 이선영 (한양대학교 자동차전자제어연구소) ;
  • 서재규 (한양대학교 자동차전자제어연구소) ;
  • 정호기 (한양대학교 미래자동차학과)
  • Lee, Sunyoung (Research Institute of Automotive Control and Electronics, Hanyang University) ;
  • Suhr, Jae Kyu (Research Institute of Automotive Control and Electronics, Hanyang University) ;
  • Jung, Ho Gi (Department of Automotive Engineering, Hanyang University)
  • 투고 : 2014.10.28
  • 심사 : 2015.07.07
  • 발행 : 2015.11.01
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초록

Recently, a medium-level representation named "Stixel" has been extensively researched in stereo vision-based environmental perception. Obstacle detection using Stixel representation consists of three steps: static Stixel generation, dynamic Stixel generation, and Stixel segmentation. This paper focuses on the Stixel segmentation step and has two contributions. One is that it shows that Stixel segmentation performance can be enhanced by utilizing both image domain and real world domain features. The other is that it suggests that parameters used for Stixel segmentation can be effectively tuned based on genetic algorithm. The proposed method was quantitatively evaluated and the result showed that the proposed method increased Stixel segmentation accuracy compared with the previous method.

키워드

참고문헌

  1. T.-N. Nguyen, B. Michaelis, A. Al-Hamadi, M. Tornow and M.-M. Meinecke, "Stereo-camera-based Urban Environment Perception Using Occupancy Grid and Object Tracking," IEEE Transactions on Intelligent Transportation Systems, Vol.13, No.1, pp.154-165, 2012. https://doi.org/10.1109/TITS.2011.2165705
  2. F. Oniga and S. Nedevschi, "Processing Dense Stereo Data Using Elevation Maps: Road Surface, Traffic Isle, and Obstacle Detection," IEEE Transactions on Vehicular Technology, Vol.59, No.3, pp.1172-1182, 2010. https://doi.org/10.1109/TVT.2009.2039718
  3. D. Pfeiffer and U. Franke, "Modeling Dynamic 3D Environments by Means of the Stixel World," IEEE Intelligent Transportation Systems Magazine, Vol.3, No.3, pp.24-36, 2011. https://doi.org/10.1109/MITS.2011.942207
  4. J. Ziegler, P. Bender, M. Schreiber, H. Lategahn, T. Strauss, C. Stiller, T. Dang, U. Franke, N. Appenrodt, C. G. Keller, E. Kaus, R. G. Herrtwich, C. Rabe, D. Pfeiffer, F. Lindner, F. Stein, F. Erbs, M. Enzweiler, C. Knoppel, J. Hipp, M. Haueis, M. Trepte, C. Brenk, A. Tamke, M. Ghanaat, M. Braun, Ar. Joos, H. Fritz, H. Mock, M. Hein and E. Zeeb, "Making Bertha Drive-An Autonomous Journey on a Historic Route," IEEE Intelligent Transportation Systems Magazine, Vol.6, No.2, pp.8-20, 2014. https://doi.org/10.1109/MITS.2014.2306552
  5. B. Gunyel, R. Benenson, R. Timofte and L. V. Gool, "Stixels Motion Estimation without Optical Flow Computation," European Conference on Computer Vision (ECCV), pp.528-539, 2012.
  6. M. Muffert, T. Milbich, D. Pfeiffer and U. Franke, "May I Enter the Roundabout? A Time-to-contact Computation Based on Stereovision," IEEE Intelligent Vehicle Symposium (IV), pp.565-570, 2012.
  7. F. Erbs, B. Schwarz and U. Franke, "Stixmentation - Probabilistic Stixel Based Traffic Scene Labeling," British Machine Vision Conference, pp.71.1-71.12, 2012.
  8. M. Ester, H.-P. Kriegel, J. Sander and X. Xu, "A Density-based Algorithm for Discovering Clusters in Large Spatial Databases with Noise," International Conference on Knowledge Discovery and Data Mining (KDD), pp.226-231, 1996.
  9. A. Geiger, M. Roser and R. Urtasun, "Efficient Large-scale Stereo Matching," ACCV2010, LNCS, Vol.6492, pp.25-38, 2011.
  10. A. D. Sappa, F. Dornaika, D. Ponsa, D. Geronimo and A. Lopez, "An Efficient Approach to Onboard Stereo Vision System Pose Estimation," IEEE Transactions on Intelligent Transportation Systems, Vol.9, No.3, pp.476-490, 2008. https://doi.org/10.1109/TITS.2008.928237
  11. R. Labayrade, D. Aubert and J.-P. Tarel, "Real Time Obstacle Detection in Stereovision on Non Flat Road Geometry Through V-disparity Representation," IEEE Intelligent Vehicle Symposium (IV), pp.17-21, 2002.
  12. S. Nedevschi, R. Danescu, D. Frentiu, T. Marita, F. Oniga, C. Pocol, T. Graf and R. Schmidt, "High Accuracy Stereovision Approach for Obstacle Detection on Non-planar Roads," IEEE Intelligent Engineering Systems (INES), pp.211-216, 2004.
  13. A. Wedel, H. Badino, C. Rabe, H. Loose, U. Franke and D. Cremers, "B-spline Modeling of Road Surfaces with an Application to Free-space Estimation," IEEE Transactions on Intelligent Transportation Systems, Vol.10, No.4, pp.572-583, 2009. https://doi.org/10.1109/TITS.2009.2027223
  14. P. Cerri and P. Grisleri, "Free Space Detection on Highways Using Time Correlation between Stabilized Sub-pixel Precision IPM Images," International Conference on Robotics and Automation (ICRA), pp.2223-2228, 2005.
  15. D. Pfeiffer and U. Franke, "Efficient Representation of Traffic Scenes by Means of Dynamic Stixels," IEEE Intelligent Vehicle Symposium (IV), pp.217-224, 2010.
  16. T. Ahonen, A. Hadid and M. Pietikainen, "Face Description with Local Binary Patterns: Application to Face Recognition," IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.28, No.12, pp.2037-2041, 2006. https://doi.org/10.1109/TPAMI.2006.244
  17. Y. J. Huang, R. Powers and G. T. Montelione, "Protein NMR Recall, Precision, and F-measure Scores (RPF Scores): Structure Quality Assessment Measures Based on Information Retrieval Statistics," Journal of the American Chemical Society, Vol.127, No.6, pp.1665-1674, 2005. https://doi.org/10.1021/ja047109h
  18. Global Optimization Toolbox, http://www.mathworks.co.kr/products/global-optimization, 2014.