Coupled 2D and 3D Analysis for Moving Objects Detection with a Moving Camera

Marie-Neige Chapel, Erwan Guillou, Saida Bouakaz

2017

Abstract

The detection of moving objects in the video stream of a moving camera is a complex task. Static objects appear moving in the video stream as moving objects. Thus, it is difficult to identify motions that belong to moving objects because they are hidden by those of static objects. To detect moving objects we propose a novel geometric constraint based on 2D and 3D information. A sparse reconstruction of the visible part of the scene is performed in order to detect motions in the 3D space where the scene perception is not deformed by the camera motion. A first labeling estimation is performed in the 3D space and then apparent motions in the video stream of the moving camera are used to validate the estimation. Labels are computed from confidence values which are updated at each frame according to the geometric constraint. Our method can detect several moving objects in complex scenes with high parallax.

References

  1. Brox, T. and Malik, J. (2011). Large displacement optical flow: Descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(3):500-513.
  2. Cui, Z., Li, A., and Jiang, K. (2014). Cooperative Moving Object Segmentation using Two Cameras based on Background Subtraction and Image Registration. Journal of Multimedia, 9(3):363-370.
  3. Elqursh, A. and Elgammal, A. (2012). Online Moving Camera Background Subtraction. In Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., and Schmid, C., editors, Computer Vision - ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI, pages 228- 241. Springer Berlin Heidelberg.
  4. Irani, M. and Anandan, P. (1998). A Unified Approach to Moving Object Detection in 2D and 3D Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(6):577-589.
  5. Jin, Y., Tao, L., Di, H., Rao, N. I., and Xu, G. (2008). Background modeling from a free-moving camera by Multi-Layer Homography Algorithm. In 2008 15th IEEE International Conference on Image Processing, pages 1572-1575.
  6. Kim, S. W., Yun, K., Yi, K. M., Kim, S. J., and Choi, J. Y. (2013). Detection of moving objects with a moving camera using non-panoramic background model. Machine Vision and Applications, 24(5):1015-1028.
  7. Narayana, M., Hanson, A., and Learned-Miller, E. (2013). Coherent Motion Segmentation in Moving Camera Videos Using Optical Flow Orientations. In Proceedings of the 2013 IEEE International Conference on Computer Vision, ICCV 7813, pages 1577-1584, Washington, DC, USA. IEEE Computer Society.
  8. Nistér, D. (2004). An Efficient Solution to the Five-Point Relative Pose Problem. IEEE Trans. Pattern Anal. Mach. Intell., 26(6):756-777.
  9. Ochs, P., Malik, J., and Brox, T. (2014). Segmentation of Moving Objects by Long Term Video Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(6):1187-1200.
  10. Romanoni, A., Matteucci, M., and Sorrenti, D. G. (2014). Background subtraction by combining Temporal and Spatio-Temporal histograms in the presence of camera movement. Machine Vision and Applications, 25(6):1573-1584.
  11. Sawhney, H. S., Guo, Y., and Kumar, R. (2000). Independent Motion Detection in 3D Scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(10):1191-1199.
  12. Sheikh, Y., Javed, O., and Kanade, T. (2009). Background subtraction for freely moving cameras. Proceedings of the IEEE International Conference on Computer Vision, pages 1219-1225.
  13. Sobral, A. and Vacavant, A. (2014). A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Computer Vision and Image Understanding, 122:4-21.
  14. Wang, J. Y. A. and Adelson, E. H. (1994). Representing moving images with layers. IEEE Transactions on Image Processing, 3(5):625-638.
  15. Xue, K., Liu, Y., Ogunmakin, G., Chen, J., and Zhang, J. (2013). Panoramic Gaussian Mixture Model and large-scale range background substraction method for PTZ camera-based surveillance systems. Machine Vision and Applications, 24(3):477-492.
  16. Yi, K. M., Yun, K., Kim, S. W., Chang, H. J., and Choi, J. Y. (2013). Detection of Moving Objects with Nonstationary Cameras in 5.8Ms: Bringing Motion Detection to Your Mobile Device. In Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 7813, pages 27- 34, Washington, DC, USA. IEEE Computer Society.
  17. Yuan, C., Medioni, G., Kang, J., and Cohen, I. (2007). Detecting Motion Regions in the Presence of a Strong Parallax from a Moving Camera by Multiview Geometric Constraints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(9):1627-1641.
  18. Zamalieva, D. and Yilmaz, A. (2014). Background subtraction for the moving camera: A geometric approach. Computer Vision and Image Understanding, 127:73- 85.
  19. Zamalieva, D., Yilmaz, A., and Davis, J. W. (2014). A Multi-transformational Model for Background Subtraction with Moving Cameras. In Fleet, D., Pajdla, T., Schiele, B., and Tuytelaars, T., editors, Computer VisionECCV 2014, number January 2014, pages 803- 817. Springer International Publishing, Cham.
Download


Paper Citation


in Harvard Style

Chapel M., Guillou E. and Bouakaz S. (2017). Coupled 2D and 3D Analysis for Moving Objects Detection with a Moving Camera . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-227-1, pages 236-245. DOI: 10.5220/0006132002360245


in Bibtex Style

@conference{visapp17,
author={Marie-Neige Chapel and Erwan Guillou and Saida Bouakaz},
title={Coupled 2D and 3D Analysis for Moving Objects Detection with a Moving Camera},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={236-245},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006132002360245},
isbn={978-989-758-227-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 6: VISAPP, (VISIGRAPP 2017)
TI - Coupled 2D and 3D Analysis for Moving Objects Detection with a Moving Camera
SN - 978-989-758-227-1
AU - Chapel M.
AU - Guillou E.
AU - Bouakaz S.
PY - 2017
SP - 236
EP - 245
DO - 10.5220/0006132002360245