Automatic Road Segmentation of Traffic Images

Chiung-Yao Fang, Han-Ping Chou, Jung-Ming Wang, Sei-Wang Chen

Abstract

Automatic road segmentation plays an important role in many vision-based traffic applications. It provides a priori information for preventing the interferences of irrelevant objects, activities, and events that take place outside road areas. The proposed road segmentation method consists of four major steps: background-shadow model generation and updating, moving object detection and tracking, background pasting, and road location. The full road surface is finally recovered from the preliminary one using a progressive fuzzy theoretic shadowed sets technique. A large number of video sequences of traffic scenes under various conditions have been employed to demonstrate the feasibility of the proposed road segmentation method.

References

  1. Ndoye, M., Totten V. F., Krogmeier, J. V., and Bullock, D. M., 2011. Sensing and signal processing for vehicle re-identification and travel time estimation. IEEE Trans. on Intelligent Transportation Systems, 12(1), pp. 119-131.
  2. Perez, J., Milanes, V., and Onieva, E., 2011. Cascade architecture for lateral control in autonomous vehicles. IEEE Trans. on Intelligent Transportation Systems, 12(1), pp. 73-82.
  3. Skog, I., and P. Händel, 2009. In-car positioning and navigation technologies - A Survey. IEEE Trans. on Intelligent Transportation Systems, 10(3), pp. 4- 21.
  4. Alvarez, J. M., Lopez, A., and Baldrich, R., 2008. Illuminant-invariant model-based road segmentation. Proc. of IEEE Intelligent Vehicles Symp., Eindhoven University of Technology Eindhoven, The Netherlands.
  5. Chen, Y. Y., & Chen, S. W., 2010. A restricted bus-lane monitoring system. Proc. of the 23rd IPPR Conf. on CVGIP, Kaohsiung, Taiwan.
  6. Chung, Y. C., Wang, J. M., Chang, C. L., and Chen, S. W., 2004. Road segmentation with fuzzy and shadowed sets. Proc. of Asian Conf. on Computer Vision, Korea.
  7. Ha, D. M., Lee, J. M., and Kim, Y. D., 2004. Neuraledge-based vehicle detection and traffic parameter extraction. Image and Vision Computing, 22(11), pp.899-907.
  8. Alvarez, J. M. and Lopez, A. M., 2011. Road detection based on illumination invariance. IEEE Trans. on Intelligent Transportation Systems, 12(1), pp. 184- 193.
  9. Courbon, J., Mezouar, Y., and Martinet, P., 2009. Autonomous navigation of vehicles from a visual memory using a generic camera model. IEEE Trans. on Intelligent Transportation Systems, 10(3), pp. 392-402.
  10. Obradovic, D., Lenz, H., and Shupfner, M., 2008. Fusion of sensor data in siemens car navigation system,” IEEE Trans. on Vehicular Technology, Vol. 56, No. 1, pp. 43-50, 2008.
  11. Danescu, R. and Nedevschi, S., 1994. Probabilistic lane tracking in difficult road scenarios using stereovision. IEEE Trans. on Intelligent Transportation Systems, 10(2), pp. 272-282.
  12. Santos, M., Linder, M., Schnitman, L., Nunes, U., and Oliveria, L., 2013. Learning to segment roads for traffic analysis in urban images. IEEE Intelligent Vehicles Symposium, pp. 527-532, Gold Coast, QLD.
  13. Tan, C., Hong, T., Chang, T., and Shneier, M., 2006. Color model-based real-time learning for road following. Proc. of the IEEE Conf. on Intelligent Transportation Systems, Toronto, Canada.
  14. Sha, Y., Zhang, G. Y., and Yang, Y., 2007. A road detection algorithm by boosting using feature combination. Proc. of IEEE Intelligent Vehicles Symposium, Istanbul, Turkey.
  15. Wang, J. M., Chung, Y. C., Lin, S. C., Chang, S. L., and Chen, S. W., 2004. Vision-based traffic measurement system. Proc. of IEEE Int'l. Conf. on Pattern Recognition, Cambridge, United Kingdom.
  16. Wang, J. M., Chung, Y. C., Lin, S. C., Chang, S. L., and Chen, S. W., 2004. Vision-based traffic measurement system. Proc. of IEEE Int'l. Conf. on Pattern Recognition, Cambridge, United Kingdom.
  17. Ma, B., Lakshmanan, S., and Hero, A. O., 2000. Simultaneous detection of lane and pavement boundaries using model-based multisensor fusion,” IEEE Trans. on Intelligent Transportation Systems, 1(3), pp. 135-147.
  18. Beucher, S. and Bilodeau, M., 1994. Road segmentation and obstacle detection by a fast watershed transform. Proc. of the Intelligent Vehicles Symp., pp. 296-301.
  19. Bilodeau, M. and Peyrard, R., 1992. Multi-pipeline architecture for real time road segmentation by mathematical morphology,” Proc. of the 2nd Prometheus Workshop on Collision Avoidance, pp. 208-214, Nurtingen, RFA.
  20. Soquet, N., Aubert, D., and Hautiere, N., 2007. Road segmentation supervised by an extended V-disparity algorithm for autonomous navigation,” Proc. of IEEE Intelligent Vehicles Symp., Istanbul, Turkey.
  21. Mackeown, W. P. J., Greenway, P., Thomas, B. T., and Wright, W. A., 1994. Road recognition with a neural network. Engineering Applications of Artificial Intelligence, 7(2), pp. 169-176.
  22. Wang, J. M., Cherng, S., Fuh, C. S., and Chen, S. W., 2008. Foreground object detection using two successive images,” IEEE Int'l. Conf. on Advanced Video and Signal based Surveillance, pp. 301-306.
  23. Paragios, N., 2006, Chapter 9: Curve Propagation, Level Set Methods and Grouping. Handbook of Mathematical Models in Computer Vision, Edited by N. Paragios, Y. Chen, and O. Faugeras, Springer Science + Business Media Inc., pp. 145-159.
  24. Comaniciu, D., Ramesh, V., and Meer, P., 2003. Kernelbased object tracking. IEEE Trans. on Pattern Analysis and Machine Intelligence, 25(5), pp. 564- 577.
  25. Chen, S. W., Chen, C. F., Chen, M. S., Cherng, S., Fang, C. Y., and Chang, K. E., 1997. Neural-fuzzy classification for segmentation of remotely sensed images,” IEEE Trans. on Signal Processing, 45(11), pp. 2639-2654.
  26. Pedrycz, W., 2009. From fuzzy sets to shadowed sets: interpretation and computing. Int'l Journal of Intelligent Systems, 24, pp. 48-61.
  27. Wang, J. M., Chen, S. W., and Fuh, C. S., 2011. Gaussian mixture of background and shadow model. Proc. of the IADIS Conf. on Computer Graphics, Visualization, Computer Vision, and Image Processing, Rome, Italy.
  28. Fritsch, J., Tobias, K., and Franz, K., 2014. Monocular road terrain detection by combining visual and spatial information. IEEE Trans. on Intelligent Transportation Systems, 15(4), pp. 1586-1596.
Download


Paper Citation


in Harvard Style

Fang C., Chou H., Wang J. and Chen S. (2015). Automatic Road Segmentation of Traffic Images . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, pages 469-477. DOI: 10.5220/0005321904690477


in Bibtex Style

@conference{visapp15,
author={Chiung-Yao Fang and Han-Ping Chou and Jung-Ming Wang and Sei-Wang Chen},
title={Automatic Road Segmentation of Traffic Images},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={469-477},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005321904690477},
isbn={978-989-758-090-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2015)
TI - Automatic Road Segmentation of Traffic Images
SN - 978-989-758-090-1
AU - Fang C.
AU - Chou H.
AU - Wang J.
AU - Chen S.
PY - 2015
SP - 469
EP - 477
DO - 10.5220/0005321904690477