Eigen-Gradients for Traffic Sign Recognition

Automatic detection and recognition of traffic sign has been a topic of great interest in advanced driver assistance system. It enhances vehicle and driver safety by providing the condition and state of the road to the drivers. However, visual occlusion and ambiguities in the real-world scenario make the traffic sign recognition a challenging task. This paper presents an Automatic Traffic Sign Detection and Recognition (ATSDR) system, involving three modules: segmentation, detection, and recognition. Region of Interest (ROI) is extracted using multiple thresholding schemes with a novel environmental selection strategy. Then, the traffic sign detection is carried out using correlation computation between log-polar mapped inner regions and the reference template. Finally, recognition is performed using Support Vector Machine (SVM) classifier. Our proposed system achieved a recognition accuracy of 98.3 % and the experimental results demonstrates the robustness of traffic sign detection and recognition in real-world scenario.

In this paper, we discuss theoretical foundations and a practical realization of a real-time traffic sign detection, tracking and recognition system operating on board of a vehicle. In the proposed framework, a generic detector refinement procedure based on mean shift clustering is introduced. This technique is shown to improve the detection accuracy and reduce the number of false positives for a broad class of object detectors for which a soft response’s confidence can be sensibly estimated. The track of an already established candidate is maintained over time using an instance-specific tracking function that encodes the relationship between a unique feature representation of the target object and the affine distortions it is subject to. We show that this function can be learned on-the-fly via regression from random transformations applied to the image of the object in known pose. Secondly, we demonstrate its capability of reconstructing the full-face view of a sign from substantial view angles. In the recognition stage, a concept of class similarity measure learned from image pairs is discussed and its realization using SimBoost, a novel version of AdaBoost algorithm, is analyzed. Suitability of the proposed method for solving multi-class traffic sign classification problems is shown experimentally for different feature representations of an image. Overall performance of our system is evaluated based on a prototype C++ implementation. Illustrative output generated by this demo application is provided as a supplementary material attached to this paper.

With the notion of smart cities transforming cities into digital societies and making people's lives easier in every way, Intelligent Transportation Systems have become an integral element among all. The Intelligent Transportation System (ITS) attempts to improve traffic efficiency by reducing congestion and ensuring the safety and comfort of commuters in real time. Traffic sign detection and recognition is one of the multifaceted conjunctive fields of research in ITS. In this paper, we address the issue of the TSR (traffic sign recognition) problem, i.e., classification of traffic signs along the roadside which plays a crucial role in developing advanced driver assistance and autonomous driving systems. CNN's network design has a huge impact on its performance and convergence. As a result, we use the Genetic Algorithm (GA) to automate the task of selecting a high-performance CNN (Convolutional Neural Network) Architecture for the GTSRB (German Traffic Sign Recognition Benchmark) dataset. The model is optimized through GA using multiple network configurations in the search space. Our model takes into account the limitations of the dataset, and we use certain data augmentation approaches to address the issues. We were able to attain an average accuracy of 98.2% which demonstrates the state-of-the-art performance on the publicly available dataset.KeywordsConvolutional neural networkGenetic algorithmMulticlass classificationDeep learningModel optimization

Detection and recognition steps are the key elements of a full traffic sign system. In this chapter, a complete description of a complete traffic sign system and how the component pieces are glued together are provided. Starting with the stereo acquisition module, data are fed into an attentional cascade detection algorithm. This step is performed separately for each video source and the coherence of the detections for the stereo pair is checked. As a result the location of the traffic sign and its size if obtained. The detected signs are normalized taking into account their shape. Then, they are given to the Forest-ECOC classification stage. All stages are validated individually and as a whole providing competitive results with respect to the state-of-the-art techniques.

Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. This paper represents a new approach for TSR system where detection of traffic sign is carried out using fuzzy rules based color segmentation method and recognition is accomplished using Speeded Up Robust Features(SURF) descriptor, trained by artificial neural network (ANN) classifier. In the detection step, the region of interest (sign area) is segmented using a set of fuzzy rules depending on the hue and saturation values of each pixel in the HSV color space, post processed to filter unwanted region. Finally the recognition of the traffic sign is implemented using ANN classifier upon the training of SURF features descriptor. The proposed system simulated on offline road scene images captured under different illumination conditions. The detection algorithm shows a high robustness and the recognition rate is quite satisfactory. The performance of the ANN model is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curves. Also, performances of some classifier such as Support Vector Machine (SVM), Decision Trees, Ensembles Learners (Adaboost) and K-Nearest Neighbor (KNN) classifier are assessed with ANN approach. The simulation results illustrate that recognition using ANN model is higher than classifiers stated above.

The ability of autonomous driving systems to recognize traffic signs in a variety of environmental conditions is crucial to their reliability. This study uses convolutional neural networks (CNNs) to provide a novel method for improving the accuracy of traffic sign recognition systems under challenging weather situations. The research focuses on developing a CNN-based model that is trained using the augmented German Traffic Sign Recognition Benchmark (GTSRB) dataset, which contains over 50,000 labeled images of road signs across 43 categories. To attempt to overcome the limitations of traditional CNN models under adverse environmental conditions, this work presents adaptive feature extraction layers that are especially made to reduce visibility problems brought on by rain, fog, and snow. By taking a comprehensive approach, the model uses advanced data augmentation methods to simulate different weather scenarios, greatly increasing the diversity of the training dataset. Through an analysis of theoretical and practical aspects, the study demonstrates how CNNs enhance the accuracy and efficiency of road sign detection systems in a different weather condition. Research evaluates the model's effectiveness using metrics such as precision, recall, and F1-score, proving its capability to reduce false positives and accurately detect relevant road sign instances. Additionally, the paper highlights the importance of careful dataset preparation and augmentation, model optimization, and training enhancements to improve the performance of road sign detection systems. The results of this research offer benefits for intelligent transport systems, autonomous driving, and road safety, pointing towards further advancements in precise and dependable road sign recognition technology.

Locating roadworks sites via detecting change in lateral positions of traffic signs measured relative to the ego-car

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

Traffic sign recognition systems based on the traditional deep learning technologies typically follow the complete data-driven mode, resulting in their unstable performances and significant security risks when applied to the real-world open scenarios. To alleviate this problem, a novel method is proposed by constructing the semantic data set based on road traffic sign design standards and using the zero-shot learning (ZSL) mechanism to develop a general TSR framework with reasoning and interpretation capabilities. This method can effectively overcome the problems of dynamic update of road traffic signs and classes missing in practice. Furthermore, the national standard for road traffic signs is used to abstract the general attributes of all classes and then the information is injected into the training process of traditional data-driven model as domain knowledge. With the help of domain knowledge, the proposed ZSL-based TSR method can recognize traffic signs that have not been seen in the training stage more accurately than random prediction and traditional deep learning models. Experimental results on the Chinese traffic sign database (CTSDB) and the German traffic sign recognition benchmark (GTSRB) demonstrate that our method, which trains a semantic auto-encoder model, can significantly improve the accuracy in traditional zero-shot learning settings. Specifically, when identifying previously unseen traffic signs in the training set, our approach achieves an improvement in accuracy of at least 29.96% and 24.25% on CTSDB and GTSRB, respectively, compared to random prediction. The study verifies the feasibility and effectiveness of the proposed scheme.

Traffic sign recognition systems (TSR) are a hot research topic because of their importance in the field of driver safety. Major car companies are developing smart driving systems for this purpose. TSR was introduced in 2022 in some luxury cars, such as the Cadillac XT4 and Mercedes-Benz GLS. TSR is a driver assistance feature that can be used to advise and warn the driver about possible road limitations along the current route. The development of the TSR system aims at two objectives; the first is to develop a fast and inexpensive system that can be added to economic vehicles. The second objective is to apply safe leadership automatically when accessing a fast and reliable TSR system. Following laws can be made compulsory by the vehicle itself, such as not exceeding speed in the event of a speed limit. (except in emergencies).There are many issues and challenges facing the researcher that works on the detection and recognition of traffic signs, such as bad weather, various illumination, occlusion, spped of cars.This research paper aims to present a proposal that facilitates access to a fast, reliable, and low-cost system. The proposed TSR is based on combining the You Only Lock Once (YOLOv5) network with a convolution neural network (CNN) that enhances the accuracy of detection and recognition in addition to speeding up the processing. Two datasets were used for training models. The YOLOv5 was trained on the GTSDB, while CNN was trained on the GTSRB.YOLOv5 is used to detect the traffic signs in the input image. The detected signs are bounded by boxes and labeled according to the group that belongs to them (the dataset is clustered into four groups (prohibitory, mandatory, danger, and others)), and then each bounded box is segmented from the rest of the image background. The segmented image (including the traffic sign) is input to the CNN to be classified into one of the 43 traffic signs.This proposed system achieved an average of 94% detection accuracy by using YOLO and 99.95% classification accuracy in CNN, with a processing time of 0.031 seconds per frame. In real-time applications, this low computation time can improve the system's stability and dependability.

Using self-organising maps in the detection and recognition of road signs

Traffic Sign Recognition (TSR) system is a vital component of intelligent transport system. It plays an important role by enhancing the safety of the drivers, pedestrians and vehicles as traffic signs provide important information of the traffic environment of the road and assist the drivers to drive more safely and easily by guiding and warning. This paper represents road sign detection and recognition system based on speeded up robust feature (SURF) descriptor that is invariant to rotation, skew and occlusion of the sign and artificial neural network (ANN) classifier. The proposed system consists of three steps. In the first step the road scene image is segmented using color based thresholding algorithm followed by some post processing like morphological analysis and filtering using region properties to extract the road sign as a blob of region. Next, shape verification is carried out by using translation, scaling and rotation invariant features vector named Distance to Borders (DtBs) of the segmented blob. Finally the recognition of the traffic sign is implemented using ANN classifier with the training of SURF descriptor. The proposed system simulated on offline road scene images shows a high success rate in the recognition stage. The performance of the classifier is illustrated in terms of cross entropy, confusion matrix and receiver operating characteristic (ROC) curve.

There is a rise in car accidents due to human errors on the road. A critical task of self-driving cars that can reduce accidents on the road is traffic sign detection and recognition (TSDR), which is vital in alerting drivers to the presence of traffic signs in advance. This research will separate the proposed deep ensemble learning algorithm into two methods. First, after the traffic scene process, the algorithm will detect the traffic sign as two categories with the YOLOv5s network. Then, process the traffic sign to recognize the traffic sign into seven classes with the MobileNet network. The detection model was trained with the Taiwan Traffic Sign Detection (TTSD) dataset collected from Taiwan roads. The recognition model was trained with the Taiwan Traffic Sign Recognition (TTSR) dataset. The result of the proposed algorithm showed high performance when experimenting with 95.83% accuracy, 87.34% true prediction, and 191.3 milliseconds (ms) of inference time.

This paper proposes the application of a swarm intelligence algorithm called Artificial Bee Colony (ABC) for the feature selection to feed a Random Forest (RF) classifier aiming to recognise Traffic Signs. In this paper, the authors define and assess several fitness functions for the feature selection stage. The idea is to minimise the correlation and maximise the entropy of a set of masks to be used for feature extraction results in a higher information gain and allows to reach recognition accuracies comparable with other state-of-art algorithms. The RF comprises as a committee based on decision trees, which allows handling large datasets and features with high performance, enabling a Traffic Sign Recognition (TSR) system oriented for real-time implementations. The German Traffic Sign Recognition Benchmark (GTSRB) was used for experiments, serving as a real basis for comparison of performance for the authors' proposal.

Traffic sign detection plays an important role in a traffic sign recognition system. It is also an unsolved problem in the intelligence transportation system. Two main technique routes are typically used in current research of the traffic sign detection: the technique using gray-level object image or that using color object image. The former has the advantage that the related theories and methods are more mature, but the weakness is short of information, which results in more chances of misdetection. The latter has the advantage that more abundant information can be provided same as human sense of vision, but the weakness is that the related theories and methods are still immaturity. This research proposes a concept of color-shape pair based on the prior characteristic of the color and the geometric shape by analyzing the traffic signs that might appear in Chinese highways. Also, a model of traffic signs, color-geometric model, is given according to the relative definition of the Color-Shape Pair. This model is constituted with 5 kinds of basic shapes and 3 kinds of basic colors of the traffic signs, focusing on the unique determinacy that is the most important characteristics of color and geometric shape. Therefore, using the detection of traffic signs based on color-geometric model, we can realize the rough classification of the traffic signs. Furthermore, we can divide 116 kinds of traffic signs on Chinese road into 7 subclasses, and reduce the complexity of recognition system of road traffic signs. The simulation experiment results have shown that this method can achieve 100% correct ratio for the traffic sign detection and the rough classification, and the robustness and real-time performance are also improved.