Stereo Vision-based Visual Tracking using 3D Feature Clustering for Robust Vehicle Tracking

Young-Chul Lim, Minsung Kang

2014

Abstract

In order to detect vehicles on the road reliably, a vehicle detector and tracker should be integrated to work in unison. In real applications, some of the ROIs generated from a vehicle detector are often ill-fitting due to imperfect detector outputs. The ill-fitting ROIs make it difficult for tracker to estimate a target vehicle correctly due to outliers. In this paper, we propose a stereo-based visual tracking method using a 3D feature clustering scheme to overcome this problem. Our method selects reliable features using feature matching and a 3D feature clustering method and estimates an accurate transform model using a modified RANSAC algorithm. Our experimental results demonstrate that the proposed method offers better performance compared with previous feature-based tracking methods.

References

  1. Adam, A., Rivlin, E., and Shimshoni, I., 2006. Robust fragments-based tracking using the integral histogram. Proceedings of IEEE Conference Computer Vision and Pattern Recognition, pp. 798-805.
  2. Bay, H., Ess, A., Tuytelaars, T., and Gool, L. V., 2008. SURF: Speeded Up Robust Features. Computer Vision and Image Understanding, Vol. 110, no. 3, pp. 346- 359.
  3. Bouguet, J. -Y., 2010. Pyramidal implementation of the Lucas-Kanade feature tracker. http://robots.stanford. edu/cs223b04/algo_tracking.pdf.
  4. Cheng, Y., 1995. Mean shift, mode seeking, and clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 17, no. 8, pp. 790-799.
  5. Comaniciu, D., Ramesh, V., and Meer, P., 2003. Kernelbased object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 25, no. 5, pp. 564-577.
  6. Jianbo, S. and Tomasi, C., 1994. Good features to track. Proceedings of IEEE Conference Computer Vision and Pattern Recognition, pp. 593-600.
  7. Khan, Z. H. and Gu, I. Y. -H., 2010. Joint feature correspondences and appearance similarity for robust visual object tracking. IEEE Transactions on Information Forensics and Security, Vol. 5, no. 3, pp. 591-606.
  8. Lim, Y. -C., Lee, M., Lee, C. -H., Kwon, S., and Lee, J. - H., 2010. Improvement of stereo vision-based position and velocity estimation and tracking using a stripebased disparity estimation and inverse perspective map-based extended Kalman filter. Optics and Lasers in Engineering, Vol. 48, no. 9, pp. 859-868.
  9. Lim, Y. -C., Lee, M., Lee, C. -H., Kwon, S., and Lee, J.- H., 2011. Integrated position and motion tracking method for online multi-vehicle tracking-by-detection. Optical Engineering, Vol. 50, no. 7, 077203.
  10. Rodrigo, R., Zouqi, M., Zhenhe, C., and Samarabandu, J., 2009. Robust and efficient feature tracking for indoor navigation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 39, no. 3, pp. 658-671.
  11. Rosten, E., Porter, R., and Drummond, T., 2010. Faster and better: a machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, no. 1, pp. 105-119.
  12. Schreiber, D., 2009. Incorporating symmetry into the Lucas-Kanade framework. Pattern Recognition Letters, Vol. 30, no. 7, pp. 690-698.
  13. Sivaraman, S. and Trivedi, M.M., 2013. A Review of Recent Developments in Vision-Based Vehicle Detection. Proceedings of IEEE Intelligent Vehicle Symposium, pp. 310-315.
  14. Xiaohe, L., Taiyi, Z. Xiaodong, S. and Jiancheng, S., 2010. Object tracking using an adaptive Kalman filter combined with mean shift. Optical Engineering Letters, Vol. 49, no. 2, 020503.
  15. Zabih, R. and Woodfill, J., 1994. Non-parametric local transforms for computing visual correspondence. Proceedings of European Conference on Computer Vision, Vol. 2, pp. 151-158.
Download


Paper Citation


in Harvard Style

Lim Y. and Kang M. (2014). Stereo Vision-based Visual Tracking using 3D Feature Clustering for Robust Vehicle Tracking . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: IVC&ITS, (ICINCO 2014) ISBN 978-989-758-040-6, pages 788-793. DOI: 10.5220/0005147807880793


in Bibtex Style

@conference{ivc&its14,
author={Young-Chul Lim and Minsung Kang},
title={Stereo Vision-based Visual Tracking using 3D Feature Clustering for Robust Vehicle Tracking},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: IVC&ITS, (ICINCO 2014)},
year={2014},
pages={788-793},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005147807880793},
isbn={978-989-758-040-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: IVC&ITS, (ICINCO 2014)
TI - Stereo Vision-based Visual Tracking using 3D Feature Clustering for Robust Vehicle Tracking
SN - 978-989-758-040-6
AU - Lim Y.
AU - Kang M.
PY - 2014
SP - 788
EP - 793
DO - 10.5220/0005147807880793