Table of Contents Author Guidelines Submit a Manuscript
Mobile Information Systems
Volume 2017, Article ID 3175186, 11 pages
https://doi.org/10.1155/2017/3175186
Research Article

Improved Object Proposals with Geometrical Features for Autonomous Driving

College of Computer, National University of Defense Technology, Changsha, China

Correspondence should be addressed to Yiliu Feng; nc.ude.tdun@11uiliygnef

Received 13 February 2017; Accepted 22 March 2017; Published 26 April 2017

Academic Editor: Zhengguo Sheng

Copyright © 2017 Yiliu Feng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '05), vol. 1, pp. 886–893, IEEE, June 2005. View at Publisher · View at Google Scholar · View at Scopus
  2. P. Felzenszwalb, D. McAllester, and D. Ramanan, “A discriminatively trained, multiscale, deformable part model,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '08), pp. 1–8, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 9, pp. 1627–1645, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Ravishankar, A. Jain, and A. Mittal, “Multi-stage contour based detection of deformable objects,” in Proceedings of the European Conference on Computer Vision (ECCV '08), pp. 483–496, Springer, 2008.
  5. P. Viola and M. J. Jones, “Robust real-time face detection,” International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, 2004. View at Publisher · View at Google Scholar · View at Scopus
  6. C. L. Zitnick and P. Dollár, “Edge boxes: locating object proposals from edges,” in Proceedings of the European Conference on Computer Vision, pp. 391–405, Springer, 2014.
  7. K. E. A. Van De Sande, J. R. R. Uijlings, T. Gevers, and A. W. M. Smeulders, “Segmentation as selective search for object recognition,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV '11), pp. 1879–1886, November 2011. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes challenge: a retrospective,” International Journal of Computer Vision, vol. 111, no. 1, pp. 98–136, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the KITTI vision benchmark suite,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 3354–3361, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. K. Yamaguchi, D. McAllester, and R. Urtasun, “Efficient joint segmentation, occlusion labeling, stereo and flow estimation,” in Computer Vision—ECCV 2014, Springer, 2014. View at Google Scholar
  11. P. Arbeláez, J. Pont-Tuset, J. Barron, F. Marques, and J. Malik, “Multiscale combinatorial grouping,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 328–335, IEEE, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Carreira and C. Sminchisescu, “Constrained parametric min-cuts for automatic object segmentation,” IEEE Transactions on Software Engineering, vol. 23, no. 3, pp. 3241–3248, 2010. View at Google Scholar
  13. B. Alexe, T. Deselaers, and V. Ferrari, “What is an object?” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '10), pp. 73–80, June 2010. View at Publisher · View at Google Scholar · View at Scopus
  14. B. Alexe, T. Deselaers, and V. Ferrari, “Measuring the objectness of image windows,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 11, pp. 2189–2202, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. M.-M. Cheng, Z. Zhang, W.-Y. Lin, and P. Torr, “BING: Binarized normed gradients for objectness estimation at 300fps,” in Proceedings of the 27th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '14), pp. 3286–3293, June 2014. View at Publisher · View at Google Scholar · View at Scopus
  16. X. Chen, K. Kundu, Y. Zhu, H. Ma, S. Fidler, and R. Urtasun, “3d Object proposals using stereo imagery for accurate object class detection,” https://arxiv.org/abs/1608.07711.
  17. S. Gupta, R. Girshick, P. Arbeláez, and J. Malik, “Learning rich features from RGB-D images for object detection and segmentation,” in Proceedings of the European Conference on Computer Vision (ECCV '14), pp. 345–360, Springer, 2014. View at Publisher · View at Google Scholar
  18. A. Janoch, S. Karayev, Y. Jia et al., “A category-level 3-D object dataset: putting the kinect to work,” in Proceedings of the IEEE International Conference on Computer Vision Workshops (ICCV '11), pp. 1168–1174, November 2011. View at Publisher · View at Google Scholar · View at Scopus