A novel method for the extraction of primary visual features from an image through intelligent feature descriptors

Abstract: As far as the safety of a driver is concerned, more focus should be put on correct interpretation and information which is conveyed by a traffic sign, while driving a vehicle along the road. A sign board can be thought of as an emblem which disseminates important and meaningful information regarding the potential hazards prevailing among road users comprising roadways cladded with snowfall, construction worksites or repairing of roads taking place and telling the people to follow an alternative route. It alerts the person who is passing through the road about the maximum possible extremity that his vehicle is trying to achieve indicating; slowing down the speed of vehicle since chances of having collision cannot be ruled out. With constant increasing of the training database size, not only the recognition accuracy, but also the computation complexity should be considered in designing a feasible recognition approach. The traffic sign images were acquired from the image database and were subjected to some pre-processing techniques such as removing the noise present in a particular image with the help of Arithmetic Mean Filter as well as Geometric Mean Filter. In the future, we will concentrate on detecting, recognizing as well as classifying a particular sign board.

Abstract: The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination.

Abstract: Detecting and recognizing traffic signs is a hot topic in the field of computer vision with lots of applications, e.g., safe driving, path planning, robot navigation etc. We propose a novel framework with two deep learning components including fully convolutional network (FCN) guided traffic sign proposals and deep convolutional neural network (CNN) for object classification. Our core idea is to use CNN to classify traffic sign proposals to perform fast and accurate traffic sign detection and recognition. Due to the complexity of the traffic scene, we improve the state-of-the-art object proposal method, EdgeBox, by incorporating with a trained FCN. The FCN guided object proposals can produce more discriminative candidates, which help to make the whole detection system fast and accurate. In the experiments, we have evaluated the proposed method on publicly available traffic sign benchmark, Swedish Traffic Signs Dataset (STSD), and achieved the state-of-the-art results.

Abstract: Pattern recognition methods are used in the final stage of a traffic sign detection and recognition system, where the main objective is to categorize a detected sign. Support vector machines have been reported as a good method to achieve this main target due to their ability to provide good accuracy as well as being sparse methods. Nevertheless, for complete data sets of traffic signs the number of operations needed in the test phase is still large, whereas the accuracy needs to be improved. The objectives of this work are to propose pre-processing methods and improvements in support vector machines to increase the accuracy achieved while the number of support vectors, and thus the number of operations needed in the test phase, is reduced. Results show that with the proposed methods the accuracy is increased 3-5% with a reduction in the number of support vectors of 50-70%.

Abstract: Graphical abstractDisplay Omitted In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods.

Abstract: 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.