PROPOSITION OF GENERIC VALIDATION CRITERIA USING STEREO-VISION FOR ON-ROAD OBSTACLE DETECTION

Mathias Perrollaz, Raphael Labayrade, Dominique Gruyer, Alain Lambert, and Didier Aubert

References

  1. [1] M. Distner, M. Bengtsson, T. Broberg, and L. Jakobsson, City safety: A system addressing rear-end collisions at low speeds, Enhanced Safety Vehicles Conf., Stuttgart, Germany, 15–18 June 2009.
  2. [2] M. Muntzinger, M. Aeberhard, S. Zuther, M. Maandhlisch, M. Schmid, J. Dickmann, and K. Dietmayer, Reliable automotive pre-crash system with out-of-sequence measurement processing, Intelligent Vehicles Symp., San Diego, CA, USA, 21–24 June 2010.
  3. [3] A.R.S. Demmel and D. Gruyer, V2v/v2i augmented maps: State-of-the-art and contribution to real-time crash risk assessment, Canadian Multidisciplinary Road Safety Conf., Niagara Falls, USA, 6–9 June 2010.
  4. [4] S. Ammoun, F. Nashashibi, and C. Laurgeau, Real-time crash avoidance system on crossroads based on 802.11 devices and gps receivers, IEEE Intelligent Transportation Systems Conf., Toronto, Canada, 17–20 September 2006.
  5. [5] B. Vanholme, D. Gruyer, B. Lusetti, S. Glaser, and S. Mammar, A legal safety concept for highly automated driving on highways, IEEE Trans. Intelligent Transportation Systems, Anchorage, USA, 16–19 September 2012.
  6. [6] C. Zott, S. Cosenza, P. Lytrivis, F. Codeca, and A. Belhoula, The safespot vehicular platform environmental perception from sensors and wireless lan messages, IEEE ITS World Congress, Stockholm, Sweden, 21–25 September 2009.
  7. [7] G. Vivo, P. Dalmasso, E. Nordin, M. Dozza, P. Cravini, F. Codec, V. Manzoni, and J. Ibanez-Guzman, V2v applications in the safespot European project: The oems experience, IEEE ITS World Congress, Stockholm, Sweden, 21–25 September 2009.
  8. [8] A. Busson, A. Lambert, D. Gruyer, and D. Gingras, Analysis of inter-vehicle communication to reduce road crashes, IEEE Trans. Vehicular Technology, 2011.
  9. [9] A. Lambert, D. Gruyer, A. Busson, and H.M. Ali, Usefulness of collision warning inter-vehicular systems, International Journal of Vehicle Safety, 5(1), 2010, 6074.
  10. [10] M. Skutek, M. Mekhaiel, and G. Wanielik, Precrash system based on radar for automotive applications, IEEE Intelligent Vehicles Symp., Columbus, USA, 9–11 June 2003.
  11. [11] R. Labayrade, C. Royere, D. Gruyer, and D. Aubert, Cooperative fusion for multi-obstacles detection with the use of stereovision and laser scanner, Autonomous Robots, 19 (2), 2005, 117–140.
  12. [12] A. Mendes, L.C. Bento, and U. Nunes, Multi-target detection and tracking with a laser scanner, IEEE Intelligent Vehicles Symp., Parma, Italy, 14–17 June 2004.
  13. [13] M. Bertozzi and A. Broggi, Gold: A parallel real-time stereo vision system for generic obstacle and lane detection, IEEE Trans. Image Processing, 7, 1998, 62–81.
  14. [14] R. Labayrade, D. Aubert, and J. Tarel, Real time obstacle detection on non flat road geometry through ‘v-disparity’ representation, IEEE Intelligent Vehicles Symp., Versailles, France, 18–20 June 2002.
  15. [15] 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 Conf., Cluj Napoca, Romania, 19–21 June 2004.
  16. [16] D. Pfeiffer and U. Franke, Efficient representation of traffic scenes by means of dynamic stixels, Intelligent Vehicles Symp., San Diego, USA, San Diego, CA, USA, 21–24 June 2010.
  17. [17] W. van der Mark and D.M. Gavrila, Real-time dense stereo for intelligent vehicles, IEEE Transactions on Intelligent Transportation Systems, 7 (1), 2006, 38–50.
  18. [18] H. Hirschmuller, Stereo processing by semiglobal matching and mutual information, IEEE Transactions on Pattern Analysis and Machine Intelligence, 30 (2), 2008, 328–341.
  19. [19] N. Dalal and B. Triggs, Histograms of oriented gradients for human detection, Int. Conf. on Computer Vision and Pattern Recognition, San Diego, CA, USA, 20–16 June 2005.
  20. [20] P. Viola, M. Jones, and D. Snow, Detecting pedestrians using patterns of motion and appearance, International Journal of Computer Vision, 63 (2), 2005, 153–161.
  21. [21] M. Enzweiler and D.M. Gavrila, Monocular pedestrian detection: Survey and experiments, IEEE Transactions on Pattern Analysis and Machine Intelligence, 31 (12), 2009.
  22. [22] A. Broggi, P. Cerri, S. Ghidoni, P. Grisleri, and H.G. Jung, A new approach to urban pedestrian detection for automatic braking, IEEE Transactions Intelligent Transportation Systems, 10 (4), 2009, 594–605.
  23. [23] D. Gavrila and S. Munder, Multi-cue pedestrian detection and tracking from a moving vehicle, International Journal of Computer Vision, 73 (1), 2007, 41–59.
  24. [24] A. Haselhoff, A. Kummert, and G. Schneider, Radar-vision fusion with an application to car-following using an improved adaboost detection algorithm, IEEE Intelligent Transportation Systems Conf., Seattle, USA, 30 September–3 oct 2007.
  25. [25] G. Toulminet, M. Bertozzi, S. Mousset, A. Bensrhair, and A. Broggi, Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis, IEEE Transactions on Image Processing, 15 (8), 2006, 2364–2375.
  26. [26] S. Rodriguez, V. Fremont, P. Bonnifait, and V. Cherfaoui, Visual confirmation of mobile objects tracked by a multi-layer lidar, IEEE Int. Conf. on Intelligent Transportation Systems, Madeira, Portugal, 19–22 September 2010.
  27. [27] M. Perrollaz, R. Labayrade, C. Roy`ere, N. Hauti`ere, and D. Aubert, Long range obstacle detection using laser scanner and stereovision, IEEE Intelligent Vehicles Symp., Tokyo, Japan, 13–15 June 2010.
  28. [28] M. Bai, Y. Zhuang, and W. Wang, Stereovision based obstacle detection approach for mobile robot navigation, Int. Conf. on Intelligent Control and Information Processing, Dalian China, 12–15 August 2010.
  29. [29] R. Labayrade, M. Perrollaz, D. Gruyer, and D. Aubert, Sensor data fusion for road obstacle detection: A validation framework, in C. Thomas (ed.), Sensor fusion and its applications (InTech, 2010).
  30. [30] D. Scharstein and R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms, International Journal of Computer Vision, 47 (1–3), 2002, 7–42.
  31. [31] G. Egnal and R. Wildes, Detecting binocular half-occlusions: Empirical comparisons of five approaches, IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (8), 2002, 1127–1133.
  32. [32] M. Perrollaz, R. Labayrade, R. Gallen, and D. Aubert, A three resolution framework for reliable road obstacle detection using stereovision, IAPR Int. Conf. on Machine Vision and Applications, Tokyo, Japan, 16–18 May 2007.

Important Links:

Go Back