Automatic Detection of Regulatory Traffic Signs via Circle Detection by Post Edge Detection Applied to Straight Line Hough Transform

Abstract

In this paper, a new effective method for automatic detection of circular traffic signs is introduced. Automatic traffic sign recognition is a crucial application of Driver Assistance Systems for safe and comfortable driving conditions. The major step of traffic sign recognition is to detect and localize the traffic signs if they exist. An important portion of traffic signs (i.e. regulatory traffic signs) has circular shape. They include vital information about the traffic rules and regulations (especially the speed limits). Therefore, this study introduces an advanced circular detection method to detect and localize the circular traffic signs. In the previous literature, it is known that a circle creates a distinctive sinusoidal structure in Straight Line Hough Transform (SLHT). This study exploits this notion in circle detection by trying to catch a part of the envelopes of this sinusoidal structure. First, post edge detection is applied to SLHT, and this image is called H-Edge image. Then, edge linking is performed on H-Edge image to obtain multiple candidate curves. By sinusoid curve fitting and sinusoidal normalization, the curves belonging to the sinusoidal structure are identified, so the circle in the original image is detected. Furthermore, a new effective iterative linear image segmentation method which is based on local minima in SLHT is proposed. Combining these two methods and color filtering, a new effective method for circular traffic sign detection is obtained. For certain sample images, the new method effectively detects and localizes the existing circular traffic signs.

Keywords

• Automatic Traffic Sign Detection,Advanced Driver Assistant Systems,Circle Detection,Image Segmentation,Straight Line Hough Transform

References

1. [1] Pascual, J. P. C. (2009). Advanced driver assistance system based on computer vision using detection, recognition and tracking of road signs, Phd Dissertation, University Carlos III de Madrid.
2. [2] Jiménez, F., Naranjo, J. E., Anaya, J. J., García, F., Ponz, A. and Armingol, J. M. (2016). Advanced driver assistance system for road environments to improve safety and efficiency, Transportation Research Procedia, 14, 2245-2254.
3. [3] Agrawal, S. and Chaurasiya, R. K. (2017). Automatic traffic sign detection and recognition using moment invariants and support vector machine, in International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE) , pp. 289-295.
4. [4] Wali, S. B., Hannan, M. A., Hussain, A. and Samad, S. A. (2015). An automatic traffic sign detection and recognition system based on colour segmentation, shape matching, and SVM, Mathematical Problems in Engineering, (No. 250461).
5. [5] Shao, F., Wang, X., Meng, F., Rui, T., Wang, D. and Tang, J. (2018). Real-time traffic sign detection and recognition method based on simplified Gabor wavelets and CNNs, Sensors, 18(10), 3192 1-24.
6. [6] Horak, K., Cip, P. and Davidek, D. (2016). Automatic traffic sign detection and recognition using colour segmentation and shape identification, MATEC Web of Conferences - EDP Sciences., 68, (No. 17002).
7. [7] Saadna, Y. and Behloul, A. (2017). An overview of traffic sign detection and classification methods, International journal of multimedia information retrieval, 6(3), 193-210.
8. [8] Swathi, M. and Suresh, K. V. (2017). Automatic traffic sign detection and recognition: A review, in 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET), pp. 1-6.

9. [9] Van Ginkel, M., Hendriks, C. L. and Van Vliet, L. J. (2004). A short introduction to the Radon and Hough transforms and how they relate to each other, The Quantitative Image Group Technical Report Series, (No. QI-2004-01).
10. [10] Duda, R. O. and Hart, P. E. (1972). Use of the Hough transformation to detect lines and curves in pictures, Communications of the ACM, 15(1), 11-15.
11. [11] Ballard, D. H. (1981). Generalizing the Hough transform to detect arbitrary shapes, Pattern recognition, 13(2), 111-122.
12. [12] Pao, D., Li, H. F. and Jayakumar, R. (1990). Detecting parameteric curves using the straight line Hough transform, 10th International Conference on Pattern Recognition, Vol. 1, pp. 620-625.
13. [13] Tiwari, S., Shukla, V. P., Biradar, S. R. and Singh, A. K (2013). Review of motion blur estimation techniques, Journal of Image and Graphics, 1(4), 176-184.
14. [14] MATLAB 2014b, The MathWorks, Inc., Natick, Massachusetts, United States, License Number: 991708.
15. [15] Murakami, K., Koshimizu, H. and Hasegawa, K. (1988). An algorithm to extract convex hull on θ-ρ Hough transform space, 9th International Conference on Pattern Recognition, pp. 500-503.
16. [16] Li, H. F., Pao, D. and Jayakumar, R. (1989). Improvements and systolic implementation of the Hough transformation for straight line detection, Pattern Recognition, 22(6), 697-706.
17. [17] Nair, P. S. and Saunders, A. T. (1996). Hough transform based ellipse detection algorithm, Pattern Recognition Letters, 17(7), 777-784.
18. [18] Chen, X., Lu, L. and Yang, S. (2014). Concentric circle detection based on normalized distance variance and the straight line Hough transform, 9th International Conference on Computer Science & Education (ICCSE), pp. 765-769.
19. [19] Okman, O. E. and Akar, G. B. (2013). A circle detection approach based on Radon Transform, International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2119-2123.
20. [20] Peng, H. and Rao, R. (2008). A novel circle detection method using Radon Transform. Image Processing: Machine Vision Applications, (No. 6813).
21. [21] Tek, F. B., Dempster, A. G. and Kale, I. (2005). Blood cell segmentation using minimum area watershed and circle radon transformations, 40 Years On Mathematical Morphology, pp. 441-454.
22. [22] Guan, P. P. and Yan, H. (2011). Blood cell image segmentation based on the Hough transform and fuzzy curve tracing, International Conference on Machine Learning and Cybernetics (ICMLC), Vol. 4, pp. 1696-1701.
23. [23] Pao, D. C. W., Li, H. F. and Jayakumar, R. (1992). Shapes recognition using the straight line Hough transform: Theory and generalization, IEEE Transactions on Pattern Analysis and Machine Intelligence, 14(11), 1076-1089.
24. [24] Kovesi P. Edge, (2017). Linking and Line Segment Fitting, https://www.peterkovesi.com/matlabfns/#edgelink.