Fast and Robust Cyclist Detection for Monocular Camera Systems

Wei Tian, Martin Lauer

2015

Abstract

References

  1. Benenson, R., Mathias, M., Timofte, R., and Van Gool, L. (2012). Pedestrian detection at 100 frames per second. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2903-2910.
  2. Bojilov, L., Alexiev, K., and Konstantinova, P. (2003). An accelerated imm jpda algorithm for tracking multiple manoeuvring targets in clutter. In Numerical Methods and Applications, volume 2542, pages 274-282. Springer Berlin Heidelberg.
  3. Breiman, L. (2001). Random forests. Machine Learning, 45(1):5-32.
  4. Cho, H., Rybski, P., and Zhang, W. (2010). Vision-based bicycle detection and tracking using a deformable part model and an ekf algorithm. In 13th International IEEE Conference on Intelligent Transportation Systems.
  5. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886- 893 vol. 1.
  6. Dollár, P., Tu, Z., Perona, P., and Belongie, S. (2009). Integral channel features. In BMVC.
  7. Dollár, P., Wojek, C., Schiele, B., and Perona, P. (2012). Pedestrian detection: An evaluation of the state of the art. PAMI, 34.
  8. Felzenszwalb, P., McAllester, D., and Ramanan, D. (2008). A discriminatively trained, multiscale, deformable part model. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8.
  9. Felzenszwalb, P. F., Girshick, R. B., and McAllester, D. (2010). Discriminatively trained deformable part models, release 4.
  10. Gandhi, T. and Trivedi, M. (2007). Pedestrian protection systems: Issues, survey, and challenges. Intelligent Transportation Systems, IEEE Transactions on, 8(3):413-430.
  11. Geiger, A., Lenz, P., and Urtasun, R. (2012). Are we ready for autonomous driving? the kitti vision benchmark suite. In Conference on Computer Vision and Pattern Recognition (CVPR).
  12. Kalman, R. E. (1960). A new approach to linear filtering and prediction problems.
  13. Li, T., Cao, X., and Xu, Y. (2010). An effective crossing cyclist detection on a moving vehicle. In Intelligent Control and Automation (WCICA), 2010 8th World Congress on, pages 368-372.
  14. Mori, G., Belongie, S., and Malik, J. (2005). Efficient shape matching using shape contexts. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(11):1832-1837.
  15. Ohn-Bar, E. and Trivedi, M. M. (2014). Fast and robust object detection using visual subcategories. In Computer Vision and Pattern Recognition Workshops-Mobile Vision.
  16. Platt, J. C. (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In ADVANCES IN LARGE MARGIN CLASSIFIERS, pages 61-74. MIT Press.
  17. Rogers, S. and Papanikolopoulos, N. (2000). Counting bicycles using computer vision. In Intelligent Transportation Systems, 2000. Proceedings. 2000 IEEE, pages 33-38.
  18. Sabzmeydani, P. and Mori, G. (2007). Detecting pedestrians by learning shapelet features. In Computer Vision and Pattern Recognition, 2007. CVPR 7807. IEEE Conference on, pages 1-8.
  19. Shu, G., Dehghan, A., Oreifej, O., Hand, E., and Shah, M. (2012). Part-based multiple-person tracking with partial occlusion handling. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 1815-1821.
  20. Sudowe, P. and Leibe, B. (2011). Efficient use of geometric constraints for sliding-window object detection in video. In Computer Vision Systems, volume 6962, pages 11-20. Springer Berlin Heidelberg.
  21. Viola, P. and Jones, M. (2004). Robust real-time face detection. International Journal of Computer Vision, 57(2):137-154.
  22. Walk, S., Majer, N., Schindler, K., and Schiele, B. (2010). New features and insights for pedestrian detection. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, pages 1030-1037.
  23. Wojek, C. and Schiele, B. (2008). A performance evaluation of single and multi-feature people detection. In Pattern Recognition, volume 5096, pages 82-91. Springer Berlin Heidelberg.
Download


Paper Citation


in Harvard Style

Tian W. and Lauer M. (2015). Fast and Robust Cyclist Detection for Monocular Camera Systems . In Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2015) ISBN , pages 3-8


in Bibtex Style

@conference{dcvisigrapp15,
author={Wei Tian and Martin Lauer},
title={Fast and Robust Cyclist Detection for Monocular Camera Systems},
booktitle={Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2015)},
year={2015},
pages={3-8},
publisher={SciTePress},
organization={INSTICC},
doi={},
isbn={},
}


in EndNote Style

TY - CONF
JO - Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2015)
TI - Fast and Robust Cyclist Detection for Monocular Camera Systems
SN -
AU - Tian W.
AU - Lauer M.
PY - 2015
SP - 3
EP - 8
DO -