Advanced Driver Assistance Systems rely on automated traffic sign recognition. Today, Deep Learning methods outperform other approaches in terms of accuracy and processing time; however, they require vast well-curated data sets for training. In this paper, we present the Austrian Highway Traffic Sign Data Set (ATSD), a comprehensive annotated set images almost all signs highways 2014, correspon...

In this project, we designed an advanced driver assistant system with three functions: traffic sign recognition, lane deviation detection, and car make identification. The traffic sign recognition implements Viola and Jones detector to accurately detect the traffic sign ahead. Hough transforms and edge detection are used in the lane deviation detection. For vehicle make identification, two mode...

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation I...

We present an accurate and efficient system for traffic sign recognition in a real-world driving scene video. The proposed system uses local structure features to achieve high, illumination-invariant accuracy in detection and recognition. We exploit a property of traffic signs, namely, shared boundary shapes, to enhance the speed and accuracy of the detection step. A multi-level SVM structure i...

Detection and recognition of traffic signs is an essential task of regulating the traffic, guiding and warning driver’s pedestrians. In traffic sign detection research are categorized into three groups. The first group of researchers believes that traffic sign detection colors are important information by which traffic signs can be detected and classified. The second group believes that detecti...

This paper reviews the popular traffic sign detection methods prevalent in recent literature. The methods are divided into three categories: color-based, shape-based, and learningbased. Color-based detection methods from eleven different works are studied and summarized in a table for easy reference. Three shape-based detection methods are presented, and a recent method based on Hough transform...

Traffic Sign Recognition (TSR) is used to regulate traffic signs, warn a driver, and command or prohibit certain actions. A 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 automated intelligent driving vehicle or driv...

We propose a novel traffic sign detection system that simultaneously estimates the location and precise boundary of traffic signs using convolutional neural network (CNN). Estimating the precise boundary of traffic signs is important in navigation systems for intelligent vehicles where traffic signs can be used as 3D landmarks for road environment. Previous traffic sign detection systems, inclu...