FAST DEPTH-INTEGRATED 3D MOTION ESTIMATION AND VISUALIZATION FOR AN ACTIVE VISION SYSTEM

M. Salah E.-N. Shafik, Bärbel Mertsching

Abstract

In this paper, we present a fast 3D motion parameter estimation approach integrating the depth information acquired by a stereo camera head mounted on a mobile robot. Afterwards, the resulting 3D motion parameters are used to generate and accurately position motion vectors of the generated depth sequence in the 3D space using the geometrical information of the stereo camera head. The proposed approach has successfully detected and estimated predefined motion patterns such as motion in the Z direction and motion vectors pointing to the robot which is very important to overcome typical problems in autonomous mobile robotic vision such as collision detection and inhibition of the ego-motion defects of a moving camera head. The output of the algorithm is part of a multi-object segmentation approach implemented in an active vision system.

References

  1. Ali, I. and Mertsching, B. (2009). Surveillance System Using a Mobile Robot Embedded in a Wireless Sensor Network . In International Conference on Informatics in Control, Automation and Robotics, volume 2, pages 293 - 298, Milan, Italy.
  2. Aziz, Z. and Mertsching, B. (2009). Visual Attention in 3D Space. In International Conference on Informatics in Control, Automation and Robotics, volume 2, pages 471 - 474, Milan, Italy.
  3. Bruhn, A., Weickert, J., Feddern, C., Kohlberger, T., and Schnorr, C. (2005). Variational optical flow computation in real time. 14(5):608-615.
  4. Gruber, A. and Weiss, Y. (2007). Incorporating non-motion cues into 3d motion segmentation. Computer Vision and Image Understanding, 108(3):261-271.
  5. Hasler, N., Rosenhahn, B., Thormhlen, T., Wand, M., Gall, J., and H.-P.Seidel (2009). Markerless motion capture with unsynchronized moving cameras. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09).
  6. Kim, J.-H., Chung, M. J., and Choi, B. T. (2010). Recursive estimation of motion and a scene model with a two-camera system of divergent view. Pattern Recognition, 43(6):2265 - 2280.
  7. Kotthäuser, T. and Mertsching, B. (2010). Validation vision and robotic algorithms for dynamic real world environments. In Simulation, Modeling and Programming for Autonomous Robots, LNAI 6472, pages 97 - 108. Springer.
  8. Li, H., Hartley, R., and Kim, J. (2008). A linear approach to motion estimation using generalized camera models. In CVPR08, pages 1-8.
  9. Massad, A., Jesikiewicz, M., and Mertsching, B. (2002). Space-variant motion analysis for an active-vision system. In Proceedings of ACIVS 2002, Ghent, Belgium.
  10. Pundlik, S. J. and Birchfield, S. T. (2006). Motion segmentation at any speed. In Proceedings of the British Machine Vision Conference, Scotland.
  11. Ribnick, E., Atev, S., and Papanikolopoulos, N. (2009). Estimating 3d positions and velocities of projectiles from monocular views. IEEE Trans. Pattern Anal. Mach. Intell., 31(5):938-944.
  12. Rosenhahn, B., Brox, T., and Weickert, J. (2007). Threedimensional shape knowledge for joint image segmentation and pose tracking. Int. J. Comput. Vision, 73(3):243-262.
  13. Schmudderich, J., Willert, V., Eggert, J., Rebhan, S., Goerick, C., Sagerer, G., and Korner, E. (2008). Estimating object proper motion using optical flow, kinematics, and depth information. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 38(4):1139 -1151.
  14. Shafik, M. and Mertsching, B. (2007). Enhanced motion parameters estimation for an active vision system. In 7th Open German/Russian Workshop on Pattern Recognition and Image Understanding, OGRW 07, Ettlingen, Germany.
  15. Shafik, M. and Mertsching, B. (2008). Fast saliency-based motion segmentation algorithm for an active vision system. In Advanced Concepts for Intelligent Vision Systems (ACIVS 2008), Juan-les-Pins, France.
  16. Shafik, M. and Mertsching, B. (2009). Real-Time ScanLine Segment Based Stereo Vision for the Estimation of Biologically Motivated Classifier Cells. In KI 2009: Advances in Artificial Intelligence, volume 5803 of LNAI, pages 89 - 96.
  17. Sotelo, M., Flores, R., Garca, R., Ocaa, M., Garca, M., Parra, I., Fernndez, D., Gaviln, M., and Naranjo, J. (2007). Ego-motion computing for vehicle velocity estimation. In Moreno-Daz, R., Pichler, F., and Quesada-Arencibia, A., editors, EUROCAST, volume 4739 of LNCS, pages 1119-1125.
  18. Taylor, J., Jepson, A., and Kutulakos, K. (2010). Non-rigid structure from locally-rigid motion. In Proceedings of Computer Vision and Pattern Recognition 2010, San Francisco, CA.
  19. Tsao, T.-R., Shyu, H.-J., Libert, J. M., and Chen, V. C. (1991). A lie group approach to a neural system for three-dimensional interpretation of visual motion. IEEE Trans. on Neural Networks, 2(1):149-155.
  20. Yamasaki, T. and Aizawa, K. (2007). Motion segmentation and retrieval for 3-d video based on modified shape distribution. EURASIP Journal on Advances in Signal Processing, 2007:Article ID 59535, 11 pages.
  21. Yang, S. and Wang, C. (2009). Multiple-model ransac for ego-motion estimation in highly dynamic environments. In IEEE International Conference on Robotics and Automation (ICRA 7809), pages 3531-3538.
Download


Paper Citation


in Harvard Style

Salah E.-N. Shafik M. and Mertsching B. (2011). FAST DEPTH-INTEGRATED 3D MOTION ESTIMATION AND VISUALIZATION FOR AN ACTIVE VISION SYSTEM . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011) ISBN 978-989-8425-47-8, pages 97-103. DOI: 10.5220/0003315100970103


in Bibtex Style

@conference{visapp11,
author={M. Salah E.-N. Shafik and Bärbel Mertsching},
title={FAST DEPTH-INTEGRATED 3D MOTION ESTIMATION AND VISUALIZATION FOR AN ACTIVE VISION SYSTEM},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)},
year={2011},
pages={97-103},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003315100970103},
isbn={978-989-8425-47-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2011)
TI - FAST DEPTH-INTEGRATED 3D MOTION ESTIMATION AND VISUALIZATION FOR AN ACTIVE VISION SYSTEM
SN - 978-989-8425-47-8
AU - Salah E.-N. Shafik M.
AU - Mertsching B.
PY - 2011
SP - 97
EP - 103
DO - 10.5220/0003315100970103