Automatic detection and recognition of traffic signs plays a crucial role in management of the traffic-sign inventory. It provides accurate and timely way to manage traffic-sign inventory with a minimal human effort. In the computer vision community the recognition and detection of traffic signs is a well-researched problem. A vast majority of existing approaches perform well on traffic signs needed for advanced drivers-assistance and autonomous systems. However, this represents a relatively small number of all traffic signs (around 50 categories out of several hundred) and performance on the remaining set of traffic signs, which are required to eliminate the manual labor in traffic-sign inventory management, remains an open question. In this paper, we address the issue of detecting and recognizing a large number of traffic-sign categories suitable for automating traffic-sign inventory management. We adopt a convolutional neural network (CNN) approach, the Mask R-CNN, to address the full pipeline of detection and recognition with automatic end-to-end learning. We propose several improvements that are evaluated on the detection of traffic signs and result in an improved overall performance. This approach is applied to detection of 200 traffic-sign categories represented in our novel dataset. Results are reported on highly challenging traffic-sign categories that have not yet been considered in previous works. We provide comprehensive analysis of the deep learning method for the detection of traffic signs with large intra-category appearance variation and show below 3 error rates with the proposed approach, which is sufficient for deployment in practical applications of traffic-sign inventory management.
To enrich the set of considered traffic signs, some approaches sample images from multiple datasets to perform the evaluation @cite_26 @cite_5 . On the other hand, a vast number of authors use their own private datasets @cite_45 @cite_6 @cite_4 @cite_43 . To the best of our knowledge, the largest set of categories was considered in the private dataset of @cite_4 , distinguishing between 131 categories of non-text traffic signs from the roads of United Kingdom.
@cite_26 @cite_4 @cite_6 @cite_43 @cite_45 @cite_5
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Abstract The robust and accurate detection of traffic signs is a challenging problem due to the many issues that are often encountered in real traffic video capturing such as the various weather conditions, shadows and partial occlusion. To address such adverse factors, in this paper, we propose a new traffic sign detection method by integrating color invariants based image segmentation and pyramid histogram of oriented gradients (PHOG) features based shape matching. Given the target image, we first extract its color invariants in Gaussian color model, and then segment the image into different regions to get the candidate regions of interests (ROIs) by clustering on the color invariants. Next, PHOG is adopted to represent the shape features of ROIs and support vector machine is used to identify the traffic signs. The traditional PHOG is sensitive to the cluttered background of traffic sign when extracting the object contour. To boost the discriminative power of PHOG, we propose introducing Chromatic-edge to enhance object contour while suppress the noises. Extensive experiments demonstrate that our method can robustly detect traffic signs under varying weather, shadow, occlusion and complex background conditions. This paper proposes a novel system for the automatic detection and recognition of traffic signs. The proposed system detects candidate regions as maximally stable extremal regions (MSERs), which offers robustness to variations in lighting conditions. Recognition is based on a cascade of support vector machine (SVM) classifiers that were trained using histogram of oriented gradient (HOG) features. The training data are generated from synthetic template images that are freely available from an online database; thus, real footage road signs are not required as training data. The proposed system is accurate at high vehicle speeds, operates under a range of weather conditions, runs at an average speed of 20 frames per second, and recognizes all classes of ideogram-based (nontext) traffic symbols from an online road sign database. Comprehensive comparative results to illustrate the performance of the system are presented. Mobile mapping systems acquire massive amount of data under uncontrolled conditions and pose new challenges to the development of robust computer vision algorithms. In this work, we show how a combination of solid image analysis and pattern recognition techniques can be used to tackle the problem of traffic sign detection in mobile mapping data. Different from the majority of existing systems, our pipeline is based on interest regions extraction rather than sliding window detection. Thanks to the robustness of local features, the proposed pipeline can withstand great appearance variations, which typically occur in outdoor data, especially dramatic illumination and scale changes. The proposed approach has been specialized and tested in three variants, each aimed at detecting one of the three categories of mandatory, prohibitory and danger traffic signs, according to the experimental setup of the recent German Traffic Sign Detection Benchmark competition. Besides achieving very good performance in the on-line competition, our proposal has been successfully evaluated on a novel, more challenging dataset of Italian signs, thereby proving its robustness and suitability to automatic analysis of real-world mobile mapping data. HighlightsThe paper presents in detail the design of a Traffic Sign Detection pipeline.Interest regions are an effective tool to feed a Traffic Sign Detection pipeline.A context-aware and a traffic light filter can effectively prune false positives.Our algorithm obtains competitive results on a public benchmark dataset.Our pipeline achieves promising results on a challenging mobile mapping dataset. In this paper we present two variant formulations of the well-known Histogram of Oriented Gradients (HOG) features and provide a comparison of these features on a large scale sign detection problem. The aim of this research is to find features capable of driving further improvements atop a preexisting detection framework used commercially to detect traffic signs on the scale of entire national road networks (1000's of kilometres of video). We assume the computationally efficient framework of a cascade of boosted weak classifiers. Rather than comparing features on the general problem of detection we compare their merits in the final stages of a cascaded detection problem where a feature's ability to reduce error is valued more highly than computational efficiency. Results show the benefit of the two new features on a New Zealand speed sign detection problem. We also note the importance of using non-sign training and validation instances taken from the same video data that contains the training and validation positives. This is attributed to the potential for the more powerful HOG features to overfit on specific local patterns which may be present in alternative video data. Abstract Traffic signs are an essential part of any circulation system, and failure detection by the driver may significantly increase the accident risk. Currently, automatic traffic sign detection systems still have some performance limitations, specially for achromatic signs and variable lighting conditions. In this work, we propose an automatic traffic-sign detection method capable of detecting both chromatic and achromatic signs, while taking into account rotations, scale changes, shifts, partial deformations, and shadows. The proposed system is divided into three stages: (1) segmentation of chromatic and achromatic scene elements using L ⁎ a ⁎ b ⁎ and HSI spaces, where two machine learning techniques ( k -Nearest Neighbors and Support Vector Machines) are benchmarked; (2) post-processing in order to discard non-interest regions, to connect fragmented signs, and to separate signs located at the same post; and (3) sign-shape classification by using Fourier Descriptors, which yield significant advantage in comparison to other contour-based methods, and subsequent shape recognition with machine learning techniques. Experiments with two databases of real-world images captured with different cameras yielded a sign detection rate of about 97 with a false alarm rate between 3 and 4 , depending on the database. Our method can be readily used for maintenance, inventory, or driver support system applications. In this paper, we design the color fused multiple features to describe a traffic sign, and we further implement this description method to detect traffic signs and to classify multi-class traffic signs. At the detection stage, we utilize the GentleAdaboost classifier to separate traffic signs from the background; at the classification stage, we implement the random forest classifier to classify multi-class traffic signs. We do the extensive experiments on the popular standard traffic sign datasets: the German Traffic Sign Recognition Benchmark and the Swedish Traffic Signs Dataset. We compare eight features which include the HOG feature, the LBP feature, the color cues and their different combinations. We also compare the popular classifiers for traffic sign recognition. The experimental results demonstrate that the color fused feature achieves better classification performance than the feature without color cues, and the GentleAdaboost classifier achieves the better comprehensive performance of the binary classification, and the random forest classifier achieves the best multi-class classification accuracy.