The paper presents a Traffic Sign Recognition (TSR) system, which can fast and accurately recognize traffic signs of different sizes in images. The system consists of two well-designed Convolutional Neural Networks (CNNs), one for region proposals of traffic signs and one for classification of each region. In the proposal CNN, a Fully Convolutional Network (FCN) with a dual multi-scale architecture is proposed to achieve scale invariant detection. In training the proposal network, a modified "Online Hard Example Mining" (OHEM) scheme is adopted to suppress false positives. The classification network fuses multi-scale features as representation and adopts an "Inception" module for efficiency. We evaluate the proposed TSR system and its components with extensive experiments. Our method obtains @math precision and @math recall on the Swedish Traffic Signs Dataset (STSD), higher than state-of-the-art methods. Besides, our system is faster and more lightweight than state-of-the-art deep learning networks for traffic sign recognition.
In the task of traffic sign detection, most state-of-the-art methods @cite_3 @cite_32 @cite_1 adopted a general pipeline where a "sliding window" is firstly employed, followed by a hand-crafted feature extractor such as HOG @cite_12 or Hough @cite_40 , and a classifier (SVM @cite_6 or Random Forests @cite_28 ). As the sliding window scheme involves exhaustive search, these methods are too time-consuming to be deployed in real-world scenarios. To tackle this issue, several Regions of Interest (ROIs) based methods @cite_4 @cite_41 @cite_39 were proposed to replace the exhaustive search and gain acceleration. In particular, @cite_39 proposed a color probability model to estimate a probability map from an input image. Based on the probability map, a Maximally Stable Extremal Regions (MSERs) detector is applied to extract traffic sign ROI proposals. This method costs only 67 @math per image in GTSDB test dataset. These hand-crafted detectors might not have a promising recall rate when they are applied in new scenes. Most recently, Aghdam al @cite_5 combined sliding window, feature extractors and classifiers into an end-to-end CNN architecture, which gains improvement on both accuracy and speed. Note that our traffic sign proposal network is partly motivated by this approach.
@cite_4 @cite_28 @cite_41 @cite_1 @cite_32 @cite_3 @cite_6 @cite_39 @cite_40 @cite_5 @cite_12
2258105403 2052533453 2317354861 2125085157
In this paper, we present a computer vision based system for fast robust Traffic Sign Detection and Recognition (TSDR), consisting of three steps. The first step consists on image enhancement and thresholding using the three components of the Hue Saturation and Value (HSV) space. Then we refer to distance to border feature and Random Forests classifier to detect circular, triangular and rectangular shapes on the segmented images. The last step consists on identifying the information included in the detected traffic signs. We compare four features descriptors which include Histogram of Oriented Gradients (HOG), Gabor, Local Binary Pattern (LBP), and Local Self-Similarity (LSS). We also compare their different combinations. For the classifiers we have carried out a comparison between Random Forests and Support Vector Machines (SVMs). The best results are given by the combination HOG with LSS together with the Random Forest classifier. The proposed method has been tested on the Swedish Traffic Signs Data set and gives satisfactory results. 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. This paper proposes a computationally efficient method for traffic sign recognition (TSR). This proposed method consists of two modules: 1) extraction of histogram of oriented gradient variant (HOGv) feature and 2) a single classifier trained by extreme learning machine (ELM) algorithm. The presented HOGv feature keeps a good balance between redundancy and local details such that it can represent distinctive shapes better. The classifier is a single-hidden-layer feedforward network. Based on ELM algorithm, the connection between input and hidden layers realizes the random feature mapping while only the weights between hidden and output layers are trained. As a result, layer-by-layer tuning is not required. Meanwhile, the norm of output weights is included in the cost function. Therefore, the ELM-based classifier can achieve an optimal and generalized solution for multiclass TSR. Furthermore, it can balance the recognition accuracy and computational cost. Three datasets, including the German TSR benchmark dataset, the Belgium traffic sign classification dataset and the revised mapping and assessing the state of traffic infrastructure (revised MASTIF) dataset, are used to evaluate this proposed method. Experimental results have shown that this proposed method obtains not only high recognition accuracy but also extremely high computational efficiency in both training and recognition processes in these three datasets. We apply Convolutional Networks (ConvNets) to the task of traffic sign classification as part of the GTSRB competition. ConvNets are biologically-inspired multi-stage architectures that automatically learn hierarchies of invariant features. While many popular vision approaches use hand-crafted features such as HOG or SIFT, ConvNets learn features at every level from data that are tuned to the task at hand. The traditional ConvNet architecture was modified by feeding 1st stage features in addition to 2nd stage features to the classifier. The system yielded the 2nd-best accuracy of 98.97 during phase I of the competition (the best entry obtained 98.98 ), above the human performance of 98.81 , using 32×32 color input images. Experiments conducted after phase 1 produced a new record of 99.17 by increasing the network capacity, and by using greyscale images instead of color. Interestingly, random features still yielded competitive results (97.33 ).