SIFT-BASED CAMERA LOCALIZATION USING REFERENCE OBJECTS FOR APPLICATION IN MULTI-CAMERA ENVIRONMENTS AND ROBOTICS

Hanno Jaspers, Boris Schauerte, Gernot A. Fink

Abstract

In this contribution, we present a unified approach to improve the localization and the perception of a robot in a new environment by using already installed cameras. Using our approach we are able to localize arbitrary cameras in multi-camera environments while automatically extending the camera network in an online, unattended, real-time way. This way, all cameras can be used to improve the perception of the scene, and additional cameras can be added in real-time, e.g., to remove blind spots. To this end, we use the Scale-invariant feature transform (SIFT) and at least one arbitrary known-size reference object to enable camera localization. Then we apply non-linear optimization of the relative pose estimate and we use it to iteratively calibrate the camera network as well as to localize arbitrary cameras, e.g. of mobile phones or robots, inside a multi-camera environment. We performed an evaluation on synthetic as well as real data to demonstrate the applicability of the proposed approach.

References

  1. Aslan, C. T., Bernardin, K., and Stiefelhagen, R. (2008). Automatic calibration of camera networks based on local motion features. ECCV-M2SFA2.
  2. Brückner, M. and Denzler, J. (2010). Active self-calibration of multi-camera systems. Proc. DAGM.
  3. Coleman, T. F. and Li, Y. (1996). An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds. SIOPT, 6(2):418-445.
  4. Frank-Bolton, P. et al. (2008). Vision-based localization for mobile robots using a set of known views. Proc. ISVC, pages 195-204.
  5. Hartley, R. I. and Zisserman, A. (2004). Multiple View Geometry in Computer Vision. Cambridge University Press, second edition.
  6. Levenberg, K. (1944). A Method for the Solution of Certain Problems in Least-Squares. Quart. Applied Math., 2:164-168.
  7. Liu, J. and Hubbold, R. (2006). Automatic camera calibration and scene reconstruction with scale-invariant features. Proc. ISVC, pages 558-568.
  8. Lowe, D. G. (1999). Object recognition from local scaleinvariant features. Proc. IEEE ICCV, pages 1150- 1157.
  9. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. IJCV, 60(2):91-110.
  10. Marquardt, D. W. (1963). An Algorithm for LeastSquares Estimation of Nonlinear Parameters. SIAP, 11(2):431-441.
  11. Nistér, D. (2004). An efficient solution to the five-point relative pose problem. TPAMI, 26:756-777.
  12. Rodehorst, V., Heinrichs, M., and Hellwich, O. (2008). Evaluation of relative pose estimation methods for multi-camera setups. Proc. Congress ISPRS.
  13. Salah, A., Morros, R., Luque, J., Segura, C., Hernando, J., Ambekar, O., Schouten, B., and Pauwels, E. (2008). Multimodal identification and localization of users in a smart environment. JMUI, 2(2):75-91.
  14. Schauerte, B., Richarz, J., Plötz, T., Thurau, C., and Fink, G. A. (2009). Multi-modal and multi-camera attention in smart environments. Proc. ICMI.
  15. Snavely, N., Seitz, S. M., and Szeliski, R. (2008). Modeling the world from internet photo collections. IJCV, 80(2):189-210.
  16. Stewénius, H., Engels, C., and Nistér, D. (2006). Recent developments on direct relative orientation. ISPRS, 60:284-294.
  17. Strecha, C., von Hansen, W., Gool, L. V., Fua, P., and Thoennessen, U. (2008). On benchmarking camera calibration and multi-view stereo for high resolution imagery. CVPR, 0:1-8.
  18. Svoboda, T., Martinec, D., and Pajdla, T. (2005). A convenient multi-camera self-calibration for virtual environments. PRESENCE: Teleoperators and Virtual Environments, 14(4):407-422.
  19. Voit, M. and Stiefelhagen, R. (2010). 3-D user-perspective, voxel-based estimation of visual focus of attention in dynamic meeting scenarios. ICMI-MLMI.
  20. Xiong, Y. and Quek, F. (2005). Meeting room configuration and multiple camera calibration in meeting analysis. Proc. ICMI.
  21. Zhang, Z. (2000). A flexible new technique for camera calibration. TPAMI, 22(11):1330-1334.
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Paper Citation


in Harvard Style

Jaspers H., Schauerte B. and Fink G. (2012). SIFT-BASED CAMERA LOCALIZATION USING REFERENCE OBJECTS FOR APPLICATION IN MULTI-CAMERA ENVIRONMENTS AND ROBOTICS . In Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM, ISBN 978-989-8425-99-7, pages 330-336. DOI: 10.5220/0003735103300336


in Bibtex Style

@conference{icpram12,
author={Hanno Jaspers and Boris Schauerte and Gernot A. Fink},
title={SIFT-BASED CAMERA LOCALIZATION USING REFERENCE OBJECTS FOR APPLICATION IN MULTI-CAMERA ENVIRONMENTS AND ROBOTICS},
booktitle={Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,},
year={2012},
pages={330-336},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003735103300336},
isbn={978-989-8425-99-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 1st International Conference on Pattern Recognition Applications and Methods - Volume 2: ICPRAM,
TI - SIFT-BASED CAMERA LOCALIZATION USING REFERENCE OBJECTS FOR APPLICATION IN MULTI-CAMERA ENVIRONMENTS AND ROBOTICS
SN - 978-989-8425-99-7
AU - Jaspers H.
AU - Schauerte B.
AU - Fink G.
PY - 2012
SP - 330
EP - 336
DO - 10.5220/0003735103300336