Traffic Sign Recognition System Research Articles

Indian Traffic Sign Detection and Recognition

Traffic Sign Recognition system is a very significant part of the Intelligent Transportation System, as traffic signs assist the drivers to drive more carefully and professionally. The main aim of this work is to present an efficient approach for detection and recognition of Indian traffic signs. Information regarding color and geometrical shape of traffic signs are utilized by the system for localizing the traffic sign in the acquired image. An RGB color saliency attention model of traffic sign makes use of an algorithm, which discriminates the sign candidate from other objects. Morphological shape filter is exploited for extracting the geometrical information of the traffic sign. Nearest neighbor matching-based recognition is performed between localized candidate features and stored Indian traffic sign database (ITSD) features. Speed up robust features (SURF) of a traffic sign is used in nearest neighbor matching to find out the resemblance between the traffic signs. System robustness is cross-examined for illumination, scale, rotation variations, similar color and shape variations, a standard data set is also considered to evaluate the system performance. The simulation results illustrate that the proposed system is working effectively under various hazardous condition.

A Survey on Traffic Sign Detection Techniques Using Text Mining

Traffic Sign Detection and Recognition (TSDR) technique is a critical step for ensuring vehicle safety. This paper provides a comprehensive survey on traffic sign detection and recognition system based on image and video data. The main focus is to present the current trends and challenges in the field of developing an efficient TSDR system. The ultimate aim of this survey is to analyze the various techniques for detecting traffic signs in real time applications. Image processing is a prominent research area, where multiple technologies are associated to convert an image into digital form and perform some functions on it, in order to get an enhanced image or to extract some useful information from it.

Logistic Regression Based Model for Improving the Accuracy and Time Complexity of ROI's Extraction in Real Time Traffic Signs Recognition System

Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system. Traffic sign recognition systems consist of an initial detection phase where images and colors are segmented and fed to the recognition phase. The most challenging process in such systems in terms of time consumption is the detection phase. The tradeoff in previous studies, which proposed different methods for detecting traffic signs, is between accuracy and computation time. Therefore, this paper presents a novel accurate and time-efficient color segmentation approach based on logistic regression. We used RGB color space as the domain to extract the features of our hypothesis; this has boosted the speed of our approach since no color conversion is needed. Our trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting conditions. The results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method.

Improving the Classification Accuracy of Accurate Traffic Sign Detection and Recognition System Using HOG and LBP Features and PCA-Based Dimension Reduction

The automatic traffic sign detection and recognition (TSDR) is one of the most useful, important and consequential method towards designing automated and driverless vehicles. It is one of the real challenges to overcome. Even in current vehicles with drivers, it is an important matter which need to be tackled for enhancing driver’s safety. In this research, we propose to use histogram-oriented gradient (HOG) and local binary patterns (LBP) approaches for feature extraction followed by PCA-based dimension reduction for accurate TSDR system. The full approach and performance are measured on Chinese traffic sign (TS) database. The development approach of the system is divided into three steps: Image processing, detection, and recognition. For the detection of traffic signs, we used an RGB color segmentation (thresholding) technique with circular Hough Transform for circular TSDR and Hough transform Algorithm for shape detection. For recognition, features are extracted using techniques HOG and LBP. Further principal component analysis (PCA) is applied for strong component extraction and classification is performed using support vector machines (SVM) classifier. The result section validates the effectiveness of using the proposed features, as the classification accuracy has been improved with respect to previously reported works on TSDR.

Autonomous Traffic Sign (ATSR) Detection and Recognition using Deep CNN

Automatic detection and recognition of traffic signs is very important and could potentially be used for driver assistance to reduce accidents and eventually in driverless automobiles. In this paper, Deep Convolutional Neural Network (CNN) is used to develop an Autonomous Traffic and Road Sign (ATRS) detection and recognition system. The proposed system works in real time detecting and recognizing traffic sign images. The contribution of this paper is also a newly developed database of 24 different traffic signs collected from random road sides in Saudi Arabia. The images were taken from different angles and including other parameters and conditions. A total of 2718 images were collected to form the database which we named Saudi Arabian Traffic and Road Signs (SA-TRS-2018). The CNN architecture was used with varying parameters in order to achieve the best recognition rates. Experimental results show that the proposed CNN architecture achieved an accuracy of 100%, thus higher than those achieved in similar previous studies.

New Dark Area Sensitive Tone Mapping for Deep Learning Based Traffic Sign Recognition.

In this paper, we propose a new Intelligent Traffic Sign Recognition (ITSR) system with illumination preprocessing capability. Our proposed Dark Area Sensitive Tone Mapping (DASTM) technique can enhance the illumination of only dark regions of an image with little impact on bright regions. We used this technique as a pre-processing module for our new traffic sign recognition system. We combined DASTM with a TS detector, an optimized version of YOLOv3 for the detection of three classes of traffic signs. We trained ITSR on a dataset of Korean traffic signs with prohibitory, mandatory, and danger classes. We achieved Mean Average Precision (MAP) value of 90.07% (previous best result was 86.61%) on challenging Korean Traffic Sign Detection (KTSD) dataset and 100% on German Traffic Sign Detection Benchmark (GTSDB). Result comparisons of ITSR with latest D-Patches, TS detector, and YOLOv3 show that our new ITSR significantly outperforms in recognition performance.

Traffic Sign Recognition based on Synthesised Training Data

To deal with the richness in visual appearance variation found in real-world data, we propose to synthesise training data capturing these differences for traffic sign recognition. The use of synthetic training data, created from road traffic sign templates, allows overcoming the problems of existing traffic sing recognition databases, which are only subject to specific sets of road signs found explicitly in countries or regions. This approach is used for generating a database of synthesised images depicting traffic signs under different view-light conditions and rotations, in order to simulate the complexity of real-world scenarios. With our synthesised data and a robust end-to-end Convolutional Neural Network (CNN), we propose a data-driven, traffic sign recognition system that can achieve not only high recognition accuracy, but also high computational efficiency in both training and recognition processes.

Classification of traffic signal system anomalies for environment tests of autonomous vehicles

Abstract In the future there will be a lot of changes and development concerning autonomous transport that will affect all participants of transport. There are still difficulties in organizing transport, but with the introduction of autonomous vehicles more challenges can be expected. Recognizing and tracking horizontal and vertical signs can cause a difficulties for drivers and, later, for autonomous systems. Environmental conditions, deformity and quality affect the perception of signals. The correct recognition results in safe travelling for everyone on the roads. Traffic signs are designed for people that is why the recognition process is harder for the machines. However, nowadays some developers try to create a traffic sign that autonomous vehicles can use. Computer identification needs further development, as it is necessary to consider cases where traffic signs are deformed or not properly placed. In the following investigation, the advantages and disadvantages of the different perception methods and their possibilities were gathered. A methodology for the classification of horizontal and vertical traffic signs anomalies that may help in designing better testing and validation environments for traffic sign recognition systems in the future was also proposed.

Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods

This paper presents a Deep Learning approach for traffic sign recognition systems. Several classification experiments are conducted over publicly available traffic sign datasets from Germany and Belgium using a Deep Neural Network which comprises Convolutional layers and Spatial Transformer Networks. Such trials are built to measure the impact of diverse factors with the end goal of designing a Convolutional Neural Network that can improve the state-of-the-art of traffic sign classification task. First, different adaptive and non-adaptive stochastic gradient descent optimisation algorithms such as SGD, SGD-Nesterov, RMSprop and Adam are evaluated. Subsequently, multiple combinations of Spatial Transformer Networks placed at distinct positions within the main neural network are analysed. The recognition rate of the proposed Convolutional Neural Network reports an accuracy of 99.71% in the German Traffic Sign Recognition Benchmark, outperforming previous state-of-the-art methods and also being more efficient in terms of memory requirements.

Design of efficient embedded system for road sign recognition

Automatic traffic sign recognition enhances driver interactivity while driving. It improves the vigilance of the driver by alarming-him/her of signs that he/she may not perceive. In this paper, an embedded real-time system for automatic traffic sign recognition is proposed. The segmentation task of an acquired scene is processed in the HSV color space. The recognition process is performed by using the Oriented fast-and-Rotated Brief features. The developed algorithm is implemented on a ZedBoard hardware platform. The detection rate reaches the value of 97.39%. The recognition rate is equal to 95.53%.

A Real-Time Chinese Traffic Sign Detection Algorithm Based on Modified YOLOv2

Traffic sign detection is an important task in traffic sign recognition systems. Chinese traffic signs have their unique features compared with traffic signs of other countries. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, we present a Chinese traffic sign detection algorithm based on a deep convolutional network. To achieve real-time Chinese traffic sign detection, we propose an end-to-end convolutional network inspired by YOLOv2. In view of the characteristics of traffic signs, we take the multiple 1 × 1 convolutional layers in intermediate layers of the network and decrease the convolutional layers in top layers to reduce the computational complexity. For effectively detecting small traffic signs, we divide the input images into dense grids to obtain finer feature maps. Moreover, we expand the Chinese traffic sign dataset (CTSD) and improve the marker information, which is available online. All experimental results evaluated according to our expanded CTSD and German Traffic Sign Detection Benchmark (GTSDB) indicate that the proposed method is the faster and more robust. The fastest detection speed achieved was 0.017 s per image.

Traffic sign recognition based on deep convolutional neural network

Traffic sign recognition (TSR) is an important component of automated driving system. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we proposed a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines Network-in-Network and residual connection was designed. Our network has 10 layers with parameters (block-layer be seen as a single layer); the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We trained our TSR network on the German Traffic Sign Recognition Benchmark (GTSRB) dataset. To reduce overfitting, we did data augmentation on the training images and employed a regularization method named dropout. We also employed a mechanism called Batch Normalization which has been proved to be efficient for accelerating the training of deep neural networks. To speed up the training, we used an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 98.96%, exceeding the human average raters.

In-vehicle augmented reality system to provide driving safety information

Improving traffic safety is one of the important goals of intelligent transportation systems. Traffic signs play a very vital role in safe driving and in avoiding accidents by informing the driver about the speed limits or possible dangers such as icy roads, imminent road works or pedestrian crossings. In-vehicle contextual Augmented reality (AR) has the potential to provide novel visual feedbacks to drivers for an enhanced driving experience. In this paper, we propose a new AR traffic sign recognition system (AR-TSR) to improve driving safety and enhance the driver’s experience based on the Haar cascade and the bag-of-visual-words approach, using spatial information to improve accuracy and an overview of studies related to the driver’s perception and the effectiveness of the AR in improving driving safety. In the first step, the region of interest (ROI) is extracted using a scanning window with a Haar cascade detector and an AdaBoost classifier to reduce the computational region in the hypothesis-generation step. Second, we proposed a new computationally efficient method to model global spatial distribution of visual words by taking into consideration the spatial relationships of its visual words. Finally, a multiclass sign classifier takes the positive ROIs and assigns a 3D traffic sign for each one using a linear SVM. Experimental results show that the suggested method could reach comparable performance of the state-of-the-art approaches with less computational complexity and shorter training time, and the AR-TSR more strongly impacts the allocation of visual attention during the decision-making phase.

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the well-maintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.

Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems

Abstract This paper presents an efficient two-stage traffic sign recognition system. First, 3D point cloud data is acquired by a LINX Mobile Mapper system and processed to automatically detect traffic signs based on their retro-reflective material. Then, classification is carried out over the point cloud projection on RGB images applying a Deep Neural Network which comprises convolutional and spatial transformer layers. This network is evaluated in three European traffic sign datasets. On the GTSRB, it outperforms previous state-of-the-art published works and achieves top-1 rank with an accuracy of 99.71%. Furthermore, a Spanish traffic sign recognition dataset is released.

MSERs와 CNN 기반의 실시간 교통표지판 인식

This paper proposes a traffic sign recognition algorithm that is robust in various environments. Color information is an important element in the traffic sign recognition system, as the performance depends on variations in weather conditions, illumination, and type of cameras used. Besides the above factors, traffic signs also differ across countries. To overcome these problems, our approach involves traffic sign detection, classification, and tracking. In the detection module, color enhancement with maximally stable extremal regions is performed to improve the extracting candidate regions of traffic signs. Support vector machine classifiers with distance to border and histogram of oriented gradient feature vectors are used to detect the traffic signs. Detected traffic signs are thereby classified using convolutional neural networks with fine-tuning. Additionally, Kalman filter-based multi-target tracking not only verifies traffic sign detection but also optimizes the detection of regions of interest. The result of traffic sign detection is 95.67% when trained on the Belgium Traffic Signs Dataset for Detection (BTSD) training dataset and tested on the Germany Traffic Signs Detection Benchmark test dataset. Moreover, while using the BTSD training dataset, the area under the curve of our method is 89.56%. In classification, the performance of INHA Traffic Signs Classification is increased to 97.48% by adding transfer learning.

In-vehicle augmented reality TSR to improve driving safety and enhance the driver’s experience

In-vehicle contextual augmented reality (AR) has the potential to provide novel visual feedbacks to drivers for an enhanced driving experience. In this paper, we propose a new AR traffic sign recognition system (AR-TSR) to improve driving safety and enhance the driver’s experience based on the Haar cascade and the Bag-of-Visual-Words approach, using spatial information to improve accuracy and an overview of studies related to the driver’s perception and the effectiveness of the AR in improving driving safety. In the first step, the region of interest (ROI) is extracted using a scanning window with a Haar cascade detector and an AdaBoost classifier to reduce the computational region in the hypothesis generation step. Second, we proposed a new computationally efficient method to model global spatial distribution of visual words by taking into consideration the spatial relationships of its visual words. Finally, a multiclass sign classifier takes the positive ROIs and assigns a 3D traffic sign for each one using a linear SVM. Experimental results show that the suggested method could reach comparable performance of the state-of-the-art approaches with less computational complexity and shorter training time, and the AR-TSR more strongly impacts the allocation of visual attention during the decision-making phase.

An overview of traffic sign detection and classification methods

Over the last few years, different traffic sign recognition systems were proposed. The present paper introduces an overview of some recent and efficient methods in the traffic sign detection and classification. Indeed, the main goal of detection methods is localizing regions of interest containing traffic sign, and we divide detection methods into three main categories: color-based (classified according to the color space), shape-based, and learning-based methods (including deep learning). In addition, we also divide classification methods into two categories: learning methods based on hand-crafted features (HOG, LBP, SIFT, SURF, BRISK) and deep learning methods. For easy reference, the different detection and classification methods are summarized in tables along with the different datasets. Furthermore, future research directions and recommendations are given in order to boost TSR’s performance.

Overview on Traffic Sign Detection

Traffic sign detection exists as the foundation and key issue for the traffic sign recognition system. Via the relevant literature review, this specific article tries to summarize the traffic sign development status and the corresponding existing problems. More specifically, this article conducts a review and discussion of the domestic and foreign detection methods based on the color and shape detections. Finally, the problems in the traffic sign detection are put forward herein and a corresponding solution is proposed.

Real-time embedded system for traffic sign recognition based on ZedBoard

This paper presents a design methodology of a real-time embedded system that processes the detection and recognition of road signs while the vehicle is moving. An efficient algorithm was proposed, which operates in two processing steps: the detection and the recognition. Regions of interest were extracted by using the Maximally Stable Extremal Regions Method. For the recognition phase, Oriented FAST and Rotated BRIEF features were used. A hardware system based on the Xilinx Zynq platform was developed. The designed system can achieve real-time video processing while assuring constraints and a high-level accuracy in terms of detection and recognition rates.