Obstacle and Planar Object Detection using Sparse 3D Information for a Smart Walker

Séverine Cloix, Viviana Weiss, Guido Bologna, Thierry Pun, David Hasler

Abstract

With the increasing proportion of senior citizens, many mobility aid devices have been developed such as the rollator. However, under some circumstances, the latter may cause accidents. The EyeWalker project aims to develop a small and autonomous device for rollators to help elderly people, especially those with some degree of visual impairment, avoiding common dangers like obstacles and hazardous ground changes, both outdoors and indoors. We propose a method of real-time stereo obstacle detection using sparse 3D information. Working with sparse 3D points, in opposition to dense 3D maps, is computationally more efficient and more appropriate for a long battery-life. In our approach, 3D data are extracted from a stereo-rig of two 2D high dynamic range cameras developed at the CSEM (Centre Suisse d'Electronique et de Microtechnique) and processed to perform a boosting classification. We also present a deformable 3D object detector for which the 3D points are combined in several different ways and result in a set of pose estimates used to execute a less ill-posed classification. The evaluation, carried out on real stereo images of obstacles described with both 2D and 3D features, shows promising results for a future use in real-world conditions.

References

  1. Bay, H., Ess, A., Tuytelaars, T., and Van Gool, L. (2008). Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3):346-359.
  2. Broggi, A., Buzzoni, M., Felisa, M., and Zani, P. (2011). Stereo obstacle detection in challenging environments: the VIAC experience. In International Conference on Intelligent Robots and System, pages 1599- 1604, San Francisco, CA, USA. IEEE Computer Society.
  3. Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). Brief: binary robust independent elementary features. In Proceedings of the 11th European conference on Computer vision: Part IV, pages 778-792, Berlin, Heidelberg. Springer-Verlag.
  4. Cloix, S., Weiss, V., Guido, B., Pun, T., and Hasler, D. (2013). Object detection and classification using sparse 3d information for a smart walker. In Swiss Vision Day 2013, Poster Session, ETH Z ürich, Switzerland.
  5. Freund, Y. and Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119-139.
  6. Harris, C. and Stephens, M. (1988). A combined corner and edge detector. In Alvey vision conference, volume 15, page 50. Manchester, UK.
  7. José, J., Farrajota, M., Rodrigues, J. M., and du Buf, J. (2011). The smartvision local navigation aid for blind and visually impaired persons. Available at http://hdl.handle.net/10400.1/892.
  8. Lacey, G. and Rodriguez-Losada, D. (2008). The evolution of guido. Robotics & Automation Magazine, IEEE, 15(4):75-83.
  9. Muja, M. and Lowe, D. G. (2009). Fast approximate nearest neighbors with automatic algorithm configuration. In International Conference on Computer Vision Theory and Application, pages 331-340. INSTICC Press.
  10. Ong, S. K., Zhang, J., and Nee, A. Y. C. (2013). Assistive obstacle detection and navigation devices for visionimpaired users. Disability and Rehabilitation: Assistive Technology, pages 1-8.
  11. Oniga, F. and Nedevschi, S. (2010). Processing dense stereo data using elevation maps: road surface, traffic isle, and obstacle detection. Vehicular Technology, IEEE Transactions on, 59(3):1172-1182.
  12. Perrollaz, M., Spalanzani, A., and Aubert, D. (2010). Probabilistic representation of the uncertainty of stereovision and application to obstacle detection. In Intelligent Vehicles Symposium (IV), 2010 IEEE, pages 313-318. IEEE.
  13. Rodríguez, A., Yebes, J. J., Alcantarilla, P. F., Bergasa, L. M., Almazán, J., and Cela, A. (2012). Assisting the visually impaired: Obstacle detection and warning system by acoustic feedback. Sensors, 12(12):17476- 17496.
  14. Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., and Szeliski, R. (2006). A comparison and evaluation of multi-view stereo reconstruction algorithms. In Conference on Computer Vision and Pattern Recognition, volume 1, pages 519-528. IEEE Computer Society.
  15. Toulminet, G., Bertozzi, M., Mousset, S., Bensrhair, A., and Broggi, A. (2006). Vehicle detection by means of stereo vision-based obstacles features extraction and monocular pattern analysis. Image Processing, IEEE Transactions on, 15(8):2364-2375.
  16. Viola, P. and Jones, M. J. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2):137-154.
  17. Weiss, V., Cloix, S., Guido, B., Hasler, D., and Pun, T. (2013). A ground change detection algorithm using colour and texture for a smart walker. In Swiss Vision Day 2013, Poster Session, ETH Z ürich, Switzerland.
Download


Paper Citation


in Harvard Style

Cloix S., Weiss V., Bologna G., Pun T. and Hasler D. (2014). Obstacle and Planar Object Detection using Sparse 3D Information for a Smart Walker . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 292-298. DOI: 10.5220/0004661602920298


in Bibtex Style

@conference{visapp14,
author={Séverine Cloix and Viviana Weiss and Guido Bologna and Thierry Pun and David Hasler},
title={Obstacle and Planar Object Detection using Sparse 3D Information for a Smart Walker},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={292-298},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004661602920298},
isbn={978-989-758-004-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Obstacle and Planar Object Detection using Sparse 3D Information for a Smart Walker
SN - 978-989-758-004-8
AU - Cloix S.
AU - Weiss V.
AU - Bologna G.
AU - Pun T.
AU - Hasler D.
PY - 2014
SP - 292
EP - 298
DO - 10.5220/0004661602920298