Real-time Stereo Vision System at Tunnel

Yuquan Xu, Seiichi Mita, Hossein Tehrani, Kazuhisa Ishimaru

Abstract

Although stereo vision has made great progress in recent years, there are limited works which estimate the disparity for challenging scenes such as tunnel scenes. In such scenes, owing to the low light conditions and fast camera movement, the images are severely degraded by motion blur. These degraded images limit the performance of the standard stereo vision algorithms. To address this issue, in this paper, we combine the stereo vision with the image deblurring algorithms to improve the disparity result. The proposed algorithm consists of three phases: the PSF estimation phase; the image restoration phase; and the stereo vision phase. In the PSF estimation phase, we introduce three methods to estimate the blur kernel, which are optical flow based algorithm, cepstrum base algorithm and simple constant kernel algorithm, respectively. In the image restoration phase, we propose a fast non-blind image deblurring algorithm to recover the latent image. In the last phase, we propose a multi-scale multi-path Viterbi algorithm to compute the disparity given the deblurred images. The advantages of the proposed algorithm are demonstrated by the experiments with data sequences acquired in the tunnel.

References

  1. Boykov, Y., Veksler, O., and Zabih, R. (2001). Fast approximate energy minimization via graph cuts. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 23(11):1222-1239.
  2. Cho, S. and Lee, S. (2009). Fast motion deblurring. In ACM Transactions on Graphics (TOG), volume 28, page 145. ACM.
  3. Fergus, R., Singh, B., Hertzmann, A., Roweis, S. T., and Freeman, W. T. (2006). Removing camera shake from a single photograph. In ACM Transactions on Graphics (TOG), volume 25, pages 787-794. ACM.
  4. Geiger, A., Roser, M., and Urtasun, R. (2011). Efficient large-scale stereo matching. In Computer VisionACCV 2010, pages 25-38. Springer.
  5. Goossens, B., Luong, H., Aelterman, J., Piz?urica, A., and Philips, W. (2010). A gpu-accelerated realtime nlmeans algorithm for denoising color video sequences. In International Conference on Advanced Concepts for Intelligent Vision Systems, pages 46-57. Springer.
  6. He, K., Sun, J., and Tang, X. (2013). Guided image filtering. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 35(6):1397-1409.
  7. Hirsch, M., Schuler, C. J., Harmeling, S., and Scholkopf, B. (2011). Fast removal of non-uniform camera shake. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 463-470. IEEE.
  8. Hirschmüller, H. (2008). Stereo processing by semiglobal matching and mutual information. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 30(2):328-341.
  9. Joshi, N., Kang, S. B., Zitnick, C. L., and Szeliski, R. (2010). Image deblurring using inertial measurement sensors. ACM Transactions on Graphics (TOG), 29(4):30.
  10. Krishnan, D. and Fergus, R. (2009). Fast image deconvolution using hyper-laplacian priors. In Advances in Neural Information Processing Systems, pages 1033- 1041.
  11. Long, Q., Xie, Q., Mita, S., Ishimaru, K., and Shirai, N. (2014a). A real-time dense stereo matching method for critical environment sensing in autonomous driving. In Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on, pages 853-860. IEEE.
  12. Long, Q., Xie, Q., Mita, S., Tehrani, H., Ishimaru, K., and Guo, C. (2014b). Real-time dense disparity estimation based on multi-path viterbi for intelligent vehicle applications. In Proceedings of the British Machine Vision Conference. BMVA Press.
  13. Rom, R. (1975). On the cepstrum of two-dimensional functions (corresp.). Information Theory, IEEE Transactions on, 21(2):214-217.
  14. Scharstein, D. and Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1-3):7-42.
  15. Son, T. T. and Seiichi, M. (2006). Stereo matching algorithm using a simplified trellis diagram iteratively and bi-directionally. IEICE transactions on information and systems, 89(1):314-325.
  16. Sun, J., Zheng, N.-N., and Shum, H.-Y. (2003). Stereo matching using belief propagation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 25(7):787-800.
  17. Tai, Y.-W. and Lin, S. (2012). Motion-aware noise filtering for deblurring of noisy and blurry images. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 17-24. IEEE.
  18. Tai, Y.-W., Tan, P., and Brown, M. S. (2011). Richardsonlucy deblurring for scenes under a projective motion path. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 33(8):1603-1618.
  19. Veksler, O. (2005). Stereo correspondence by dynamic programming on a tree. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 2, pages 384-390. IEEE.
  20. Wang, R. and Tao, D. (2014). Recent progress in image deblurring. arXiv preprint arXiv:1409.6838.
  21. Whyte, O., Sivic, J., and Zisserman, A. (2011). Deblurring shaken and partially saturated images. In Computer Vision Workshops (ICCV Workshops), 2011 IEEE International Conference on, pages 745-752. IEEE.
  22. Xu, L. and Jia, J. (2010). Two-phase kernel estimation for robust motion deblurring. In Computer Vision-ECCV 2010, pages 157-170. Springer.
  23. Xu, Y., Hu, X., Wang, L., and Peng, S. (2012). Single image blind deblurring with image decomposition. In Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, pages 929- 932. IEEE.
  24. Zabih, R. and Woodfill, J. (1994). Non-parametric local transforms for computing visual correspondence. In Computer VisionlECCV'94 , pages 151-158. Springer.
  25. Zbontar, J. and LeCun, Y. (2014). Computing the stereo matching cost with a convolutional neural network. arXiv preprint arXiv:1409.4326.
  26. Zheng, S., Xu, L., and Jia, J. (2013). Forward motion deblurring. In Proceedings of the IEEE International Conference on Computer Vision, pages 1465-1472.
Download


Paper Citation


in Harvard Style

Xu Y., Mita S., Tehrani H. and Ishimaru K. (2017). Real-time Stereo Vision System at Tunnel . 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 402-409. DOI: 10.5220/0006112304020409


in Bibtex Style

@conference{visapp17,
author={Yuquan Xu and Seiichi Mita and Hossein Tehrani and Kazuhisa Ishimaru},
title={Real-time Stereo Vision System at Tunnel},
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={402-409},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006112304020409},
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 - Real-time Stereo Vision System at Tunnel
SN - 978-989-758-227-1
AU - Xu Y.
AU - Mita S.
AU - Tehrani H.
AU - Ishimaru K.
PY - 2017
SP - 402
EP - 409
DO - 10.5220/0006112304020409