Experts of probabilistic flow subspaces for robust monocular odometry in urban areas

IEEE Intelligent Vehicles Symposium, Proceedings

Volumn , Issue , 2012, Pages 661-667

  Herdtweck, Christian ^a   Curio, Cristóbal ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; DEPTH CUE; DEPTH PROFILE; EGO-MOTION; EGO-MOTION ESTIMATION; EXPERT MODEL; FUNDAMENTAL COMPONENT; GROUND TRUTH; MEASUREMENT NOISE; MONOCULAR IMAGE; ODOMETRY; ON-LINE ESTIMATION; PROBABILISTIC LEARNING; QUALITY MEASURES; SCENE STRUCTURE; SELECTION RULES; STATISTICAL VARIATIONS; SUBSPACE FLOW; TEST SEQUENCE; TWO-COMPONENT; UNSUPERVISED CLUSTERING; URBAN AREAS; VISUAL ODOMETRY;

ESTIMATION; INTELLIGENT VEHICLE HIGHWAY SYSTEMS;

COMPUTER VISION;

EID: 84864982278     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IVS.2012.6232238     Document Type: Conference Paper

References (17)

1. A. Wedel, C. Rabe, T. Vaudrey, T. Brox, U. Franke, and D. Cremers, "Efficient Dense Scene Flow from Sparse or Dense Stereo Data," in ECCV, ser. LNCS, D. Forsyth, P. Torr, and A. Zisserman, Eds., vol. 5302, no. 1, University of Auckland. Computer Science Department, The University of Auckland, New Zealand, 2008, pp. 739-751.
2. H. Badino, U. Franke, and D. Pfeiffer, "The Stixel World - A Compact Medium Level Representation of the 3D-World," in Pattern Recognition, ser. Lecture Notes in Computer Science, J. Denzler, G. Notni, and H. Süße, Eds. Springer Berlin / Heidelberg, 2009, vol. 5748, pp. 51-60.
3. A. Geiger, M. Roser, and R. Urtasun, "Efficient Large-Scale Stereo Matching," in Asian Conference on Computer Vision, ser. Lecture Notes in Computer Science, R. Kimmel, R. Klette, and A. Sugimoto, Eds., vol. 6492. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 25-38.
4. R. Roberts, C. Potthast, and F. Dellaert, "Learning general optical flow subspaces for egomotion estimation and detection of motion anomalies," in IEEE Conference on Computer Vision and Pattern Recognition. Miami: IEEE, Jun. 2009, pp. 57-64.
5. M. Irani, "Multi-frame optical flow estimation using subspace constraints," in Proceedings ICCV, vol. 1. IEEE, 1999, pp. 626-633.
6. M. E. Tipping and C. M. Bishop, "Probabilistic principal component analysis for metabolomic data." Journal of the Royal Statistical Society, Series B (Statistical Methodology), vol. 61, no. 3, pp. 611- 622, Jan. 1999.
7. B. Kitt and A. Geiger, "Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme," Intelligent Vehicles Symposium, 2010.
8. A. Geiger, M. Roser, and R. Urtasun, "Efficient Large-Scale Stereo Matching," in Asian Conference on Computer Vision 2010, ser. Lecture Notes in Computer Science, R. Kimmel, R. Klette, and A. Sugimoto, Eds., vol. 6492. Berlin, Heidelberg: Springer, 2011, pp. 25-38.
9. S. J. Pan and Q. Yang, "A Survey on Transfer Learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, 2010.
10. G. Brown, "Ensemble learning," in Encyclopedia of Machine Learning, 1st ed., C. Sammut and G. Webb, Eds. Springer, 2010, ch. 400.
11. R. Rosales and S. Sclaroff, "Learning body pose via specialized maps," in NIPS, 2001, pp. 1263-1270.
12. C. Nakajima, M. Pontil, B. Heisele, and T. Poggio, "Full-body person recognition system," Pattern Recognition, vol. 36, no. 9, pp. 1997- 2006, 2003.
13. H. Shimizu and T. Poggio, "Direction estimation of pedestrian from multiple still images," in In Proc. IEEE Intelligent Vehicles Symposium 2004, 2004, pp. 596-600.
14. D. M. Gavrila and S. Munder, "Multi-cue pedestrian detection and tracking from a moving vehicle," International Journal of Computer Vision, vol. 73, no. 1, pp. 41-59, 2007.
15. M. Enzweiler and D. M. Gavrila, "Integrated pedestrian classification and orientation estimation," in CVPR, 2010, pp. 982-989.
16. A. Geiger, J. Ziegler, and C. Stiller, "StereoScan: Dense 3d Reconstruction in Real-time," in IEEE Intelligent Vehicles Symposium, 2011.
17. C. M. Bishop, Pattern recognition and machine learning, M. Jordan, J. Kleinberg, and B. Schölkopf, Eds. Springer New York, 2006, vol. 4.