Browsing by Author "Tora, Moumita Roy"

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LVQ and HOG based speed limit traffic signs detection and categorization
(© 2014 IEEE Computer Society, 2014) Biswas, Rubel; Tora, Moumita Roy; Bhuiyan, Farazul Haque
The proper identification of the traffic signs can ensure driving safety and can play a very important role in reducing the number of road accidents significantly. This paper represents a uniform way to detect the speed limit traffic signs and to confirm it by recognizing the sign's speed number. In this system, firstly the red color objects are segmented from an image using LVQ. Secondly, detected circular part is extracted from the color segmented image using bounding box and then Histogram Oriented Gradient (HOG) is used to collect the feature of the extracted part of circular object and finally SVM classifier is applied to train the HOG features of each speed no. into their corresponding classes. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 200 images which were collected in different light conditions. To check the robustness of this system, it was tested against 381 images which contain 361 Speed Limit traffic sign and 30 Non- Speed Limit signs. It was found that the accuracy of recognition was 92.75% which indicates clearly the high robustness targeted by this system.
LVQ and HOG based speed limit traffic signs detection and categorization
(© 2014 IEEE Computer Society, 2014) Biswas, Rubel; Tora, Moumita Roy; Bhuiyan, Farazul Haque
The proper identification of the traffic signs can ensure driving safety and can play a very important role in reducing the number of road accidents significantly. This paper represents a uniform way to detect the speed limit traffic signs and to confirm it by recognizing the sign's speed number. In this system, firstly the red color objects are segmented from an image using LVQ. Secondly, detected circular part is extracted from the color segmented image using bounding box and then Histogram Oriented Gradient (HOG) is used to collect the feature of the extracted part of circular object and finally SVM classifier is applied to train the HOG features of each speed no. into their corresponding classes. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 200 images which were collected in different light conditions. To check the robustness of this system, it was tested against 381 images which contain 361 Speed Limit traffic sign and 30 Non- Speed Limit signs. It was found that the accuracy of recognition was 92.75% which indicates clearly the high robustness targeted by this system.
LVQ and HOG based speed limit traffic signs detection and categorization
(© 2014 IEEE Computer Society, 2014) Biswas, Rubel; Tora, Moumita Roy; Bhuiyan, Farazul Haque
The proper identification of the traffic signs can ensure driving safety and can play a very important role in reducing the number of road accidents significantly. This paper represents a uniform way to detect the speed limit traffic signs and to confirm it by recognizing the sign's speed number. In this system, firstly the red color objects are segmented from an image using LVQ. Secondly, detected circular part is extracted from the color segmented image using bounding box and then Histogram Oriented Gradient (HOG) is used to collect the feature of the extracted part of circular object and finally SVM classifier is applied to train the HOG features of each speed no. into their corresponding classes. In general, the system detects the prohibitory traffic sign in the first place, specifies whether the detected sign is a speed limit sign, and then determines the allowed speed in case the detected sign is a speed limit sign. The SVM classifier was trained with 200 images which were collected in different light conditions. To check the robustness of this system, it was tested against 381 images which contain 361 Speed Limit traffic sign and 30 Non- Speed Limit signs. It was found that the accuracy of recognition was 92.75% which indicates clearly the high robustness targeted by this system.
Warning traffic sign detection using learning vector quantization & hough transform and recognition based on HOG
(BRAC University, 2014-01) Tora, Moumita Roy; Biswas, Rubel
Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. Fast real-time and robust automatic traffic sign detection and recognition can support and disburden the driver and significantly increase driving safety and comfort. Automatic recognition of traffic signs is also important for an automated intelligent driving vehicle or for driver assistance systems. This paper aims to present a color segmentation approach for traffic sign recognition based on LVQ neural network and also focuses on triangular edge detection and feature extraction based on Hough transformation and HOG respectively. At first samples of images in different weather conditions are collected and then RGB images are converted into HSV color space. The samples are then trained using LVQ depending on the hue and saturation values of each pixel and then tested for color segmentation. The edges of the triangular segmented images are then detected using Hough Transformation. Then samples are taken to extract features using HOG. Finally they are trained and tested using SVM to get the output image. The algorithms were applied to around 100 sampled images taken in different countries and varied weather conditions. Despite the varying conditions, the algorithms worked almost accurately in all situations and the success rate was quite satisfactory with a very good response time of a few milliseconds.