Robust Zebra-Crossing Detection for Autonomous Land Vehicles and Driving Assistance Systems

Abstract:

Road scene understanding is critical for driving assistance systems and autonomous land vehicles. The main function of road scene understanding is robustly detecting useful visual objects existing in a road scene. A zebra crossing is a typical pedestrian crossing used in many countries around the world. When detecting a zebra crossing, an autonomous lane vehicle is normally required to automatically slow down its speed and to trigger a path-planning strategy for passing the zebra crossing. Also, most of driving assistance systems can send an early-warning signal to remind drivers to be more careful. This paper proposes a robust zebra-crossing detection algorithm for autonomous land vehicles and driving assistance systems. Firstly, an inverse perspective map is generated by utilizing camera calibration parameters to obtain a bird-eye view road image. Secondly, a course-to-fine detection process is applied to obtain a candidate zebra-crossing region and finally a true zebra-crossing region is recognized by combining appearance and shape features. Experiments on several kinds of real road videos which also include several challenge scenes demonstrate the effectiveness and efficiency of the proposed method.

• Periodical: Applied Mechanics and Materials (Volumes 556-562)
• Pages: 2732-2739
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.556-562.2732
• Online since: May 2014
• Authors: Chao Wang*, Huan Wang, Rui Li Wang, Chun Xia Zhao
• Keywords: Autonomous Land Vehicle, Driving Assistance System, Zebra-Crossing Detection

References

[1] I. Volodymyr, C. James, and H. Y. Shen, in: Detecting and location crosswalks using a camera phone, edited in Computer vision and pattern recognition workshops(CVPRW'08)/IEEE computer society conference on oubkusing, pp.1-8, (2008).

DOI: 10.1109/cvprw.2008.4563143

[2] I. Volodymyr, C. James, and H. Y. Shen, in: Real-time walk light detection with a mobile phone, edited in proceedings of the 12th international conference on computers helping people with special needs(ICCHP'10), pp.229-234, Berlin, Heidelberg, (2010).

DOI: 10.1007/978-3-642-14100-3_34

[3] I. Volodymyr, C. James, and H. Y. Shen, in: Crosswatch: A camera phone system for orienting visually impaired pedestrians at traffic intersections, edited in proceedings of the 11th international conference on computer helping people with special needs(ICCHP'08), pp.1122-1128, Berlin, Heidelberg, (2008).

DOI: 10.1007/978-3-540-70540-6_168

[4] B. Jason, in: Crosswalk detection utilizing lane following algorithms in OpenCV/Android, (2012).

[5] S. Stephen, in: Zebra-crossing detection for the partially sighted, edited in proceedings of international conference on Computer vision and pattern recognition(CVPR'00), pp.211-217, (2000).

DOI: 10.1109/cvpr.2000.854787

[6] A. Borkar, M Hayes, M. T. Stmth, in: Detecting lane markers in complex urban environments, edited in IEEE 7th international conference on mobile Adhoc and sensor systems, (2010).

DOI: 10.1109/mass.2010.5663812

[7] M. S. Javadi, M. A. Hannan, S. A. Samad and A. Hussain, in: A robust vision-based lane boundaries detection approach for intelligent vehicles, edited in Information Technology Journal, pp.1-9, (2012).

DOI: 10.3923/itj.2012.1184.1192

[8] R. Y. Tsai, in: An efficient and accurate camera calibration technique for 3D machine vision, Proc, edited in IEEE international conference of computer vision and pattern recognition, pp.364-374, (1986).

[9] Z. Y. Zhang, in: A flexible new technique for camera calibration, edited in IEEE Trans. on pattern analysis and machine intelligence, Vol. 22, No. 11, pp.1330-1334, (2000).

DOI: 10.1109/34.888718

[10] Y. Alex, R. Deepu, K. L. Maylor and R. Sausanto, in: Object recognition by discriminative combinations of line segments, ellipses, and appearance features, edited in IEEE Transactions on pattern analysis and machine intelligence, Vol. 34, No. 9, pp.1758-1772, (2012).

DOI: 10.1109/tpami.2011.220

[11] G. Rafael, J. Jeremie, M. Jean and R. Gregory, in: LSD: A fast line segment detector with a false detection control, edited in IEEE Transaction on pattern analysis and machine intelligent, Vol. 32, No. 4, pp.722-732, (2010).

DOI: 10.1109/tpami.2008.300

[12] M. Zuliani, C. S. Kenney and B. S. Manjunath, in: The multi-RANSAC and its application to detect plannar homographies, edited in International Conference on Image Processing, (2005).

DOI: 10.1109/icip.2005.1530351

[13] P. Viola and M. Jones, in: Rapid object detection using a boosted cascade of simple features, edited in Proc. of the IEEE conf. on computer vision and pattern recognition, pp.511-518, (2001).

DOI: 10.1109/cvpr.2001.990517

[14] T. Ojala, M. Pietikainen, and D. Harwood, in: A comparative study of texture measures with classification based on feature distributions, edited in Pattern recognition, Vol. 298, pp.51-59, (1996).

DOI: 10.1016/0031-3203(95)00067-4

[15] S.U. Mohammad and S. Tadayoshi, in: Detection of pedestrian crossing using bipolarity feature-an image based technique, edited in IEEE Transactions on Intelligent transportation systems, Vol. 6, No. 4, pp.439-445, (2005).

DOI: 10.1109/tits.2005.858787

[16] S.U. Mohammad and S. Tadayoshi, in: Robust zebra-crossing detection using bipolarity and projective invariant, edited in Proceedings of the Eighth International Symposium on Signal Processing and Its Applications, pp.571-574, (2005).

DOI: 10.1109/isspa.2005.1581002