International Journal of Technical Research (IJTR) Vol. 4, Issue 2, Jul-Aug 2015 DETECTION OF ROAD SIDE TRAFFIC SIGN

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1 DETECTION OF ROAD SIDE TRAFFIC SIGN USINGCOLOUR IMAGE SEGMENTATION Sunil kumar 1, Sandeep Singh 2 1 M.Tech Scholar, 2 Sandeep singh, Assistant Professor 1,2 ECE Deptt, OITM Juglan (HISSAR) 1 sushil@gmail.com, 2 sandeep@gmail.com Abstract Recognition of traffic signs is carried out using a fuzzy shape recogniser. Based on four shape measures - the rectangularity, triangularity, ellipticity, and octagonality, fuzzy rules were developed to determine the shape of the sign. Among these shape measures octangonality has been introduced in this research. The final decision of the recognizer is based on the combination of both the colour and shape of the sign. Keywords Fuzzy, RGB, PSNR, Traffic Sign. I. INTRODUCTION Road and traffic signs must be properly installed in the necessary locations and an inventory of them is ideally needed to help ensure adequate updating and maintenance. Meetings with the highway authorities in both Scotland and Sweden revealed the absence of but a need for an inventory of traffic signs. An automatic means of detecting and recognising traffic signs can make a significant contribution to this goal by providing a fast method of detecting, classifying and logging signs. This method helps to develop the inventory accurately and consistently. Once this is done, the detection of disfigured or obscured signs becomes easier for human operator. Colour provides powerful information for object recognition. Segmentation based on colour provides good discrimination between material boundaries. Road signs use colours to represent the key information provided to road users. As colours are distinguishing features of traffic signs, they can simplify the recognition. In addition, colour processing can significantly reduce the amount of false edge points produced by low-level image processing operations. algorithm, and recognising the traffic sign. This module normalises the recognised traffic sign so that it becomes invariant to the in-plane transformations. This means that the resultant sign has a fixed size and it is located in a standard position where its centre of gravity is located in the centre of the image. This module works in two modes; the training and the prediction mode. In the training mode it is invoked to create or update the training image database. In the prediction mode it prepares every object in the binary image to be in standard format and ready for feature extraction. This module contains algorithms which are used to extract features from either the training images in the training database or images directly from the shape analysis unit. It allows the classifier to be trained by either binary images or by features. Among the features which can be used are geometric moments, Zernike moments, Legendre moments, Orthogonal Fourier-Mellin Moments and Binary Haarfeatures. IMAGE- 1 III. RESULTS II. ALGORITHM Colour segmentation is an important step to eliminate all background objects and unimportant information in the image. It generates a binary image containing the road signs and any other objects similar to the colour of the road sign. This step reduces the amount of calculation needed in the following steps as it radically reduces the number of probable objects. A colour segmentation algorithm should be robust enough to work in a wide spectrum of environmental conditions and be able to generate binary images even when traffic sign colours are attenuated.main task of this module includes cleaning the binary image from and small objects, applying connected components labelling Fig: 1 input image for analysis Resolution 800x707, 52.7kb size RGB image The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is ISSN Page 43

2 Fig: 2 Split the original image into red bands. Red, Blue, Green bands. Fig: 5 image in red mask After the Threshold is decided for redmask:- between redband and red threshold the above output is displayed as shown in above figure. Fig: 6 representation of image in green mask Fig: 3 splitting of the image into green bands. After the Threshold is decided for green mask:- between green band and green threshold the above output is displayed as shown in above figure. Fig: 4 splitting of the image into blue bands. Splitting the original image into blue band color bands out Fig: 7 representation of image in blue mask After the Threshold is decided for blue mask:- between blue band and blue threshold the above output is displayed as shown in above figure. ISSN Page 44

3 Fig: 10 Input image Fig: 8 representation of image in red mask After the Threshold is decided for red mask:- between red band and red threshold the above output is displayed as shown in above figure. The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is Fig: 9 representation of image in red mask in color After the Threshold is decided for red mask:- between red band and red threshold the above output is displayed as shown in above figure The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is Fig: 11 Red band Red, Blue, Green bands IMAGE 2 Fig: 12 Green band ISSN Page 45

4 Fig: 13 Blue band Fig: 16 Blue mask Splitting the original image into blue band color bands out Fig: 14 Red mask Fig: 17 Red object mask Fig: 15 Green mask Fig: 18 Masked original image The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is IMAGE 3 ISSN Page 46

5 Fig: 19 Input image The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is Fig: 22 Blue band Splitting the original image into blue band color bands out Fig: 20 Red band Red, Blue, Green bands Fig: 23 Red mask Fig: 21 Green band Fig: 24 Green mask The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is ISSN Page 47

6 IMAGE 4 International Journal of Technical Research (IJTR) Fig: 25 Input image The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is Fig: 28 Blue band Splitting the original image into blue band color bands out Fig: 29 Red mask Fig: 26 Red band Red, Blue, Green bands Fig: 30 Blue mask The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is Fig: 27 Green band ISSN Page 48

7 Fig: 31 Input image. The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is Fig: 34 Red mask Fig: 35 Green mask Fig: 32 Red band Red, Blue, Green bands Fig: 33 Green band The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is Name Type of result Input image1 Psnr by salt and snr Psnr by poission snr Input image2 Psnr by salt and snr Psnr by poission snr Input image3 Psnr by salt and snr Psnr by poission snr ISSN Page 49

8 Out image1 Out image2 Out image3 International Journal of Technical Research (IJTR) Psnr by salt and snr Psnr by poission snr Psnr by salt and snr Psnr by poission snr Psnr by salt and snr Psnr by poission snr Dr.Dipti Shah, ParulSindha: Traffic Sign Detection and Recognition System Using Translation of Images in Volume 4, Issue 10 October 2014 on page number , ISSN: X. The Peak-SNR value by salt and is The SNR value is The Peak-SNR value by poisson is The SNR value is IV. CONCLUSIONS Theroad and traffic sign recognition system which can help in creating a road sign inventory was developed, implemented and evaluated. This system, which involves a mixture of computer vision and pattern recognition problems, was able to extract road signs from still images of complex scenes subject to uncontrollable illumination. In the computer vision part, algorithms were developed to segment the image by using colours and to recognise the sign by colour-shape combinations as a priori knowledge. In the pattern recognition part, two SVM classifiers were invoked to put the unknown sign in one of the traffic sign categories depending on the sign rim and interior. REFERENCES 1. DibyaJyotiBora, Anil Kumar Gupta, Ph.D. A New Approach towards Clustering based Color Image Segmentation. International Journal of Computer Applications, Volume 107 No 12, December pp Huda Noor Dean,Jabir K.V.T: Real Time Detection and Recognition of Indian Traffic Signs using Matlab: International Journal of Scientific & Engineering Research, Volume 4, Issue 5, May 2013 Page rtogujarat.gov.in 5. Dr.Dipti Shah, ParulSindha :Color detection in real time traffic sign detection and recognition system in volume 3 issue 7 July 2013 on page number 70, ISSN X. 6. Dr.Dipti Shah, ParulSindha : Shape Identification Using Centroid in Real Time Traffic Sign Detection and Recognition:Volume 4, Issue 3 March 2014 on page number , ISSN: X. ISSN Page 50