Review of Traffic Sign Detection and Recognition Techniques

Text, as one of the most compelling developments of humankind, has assumed a significant job in human life, so distant from antiquated occasions. The rich and exact data epitomized in content is extremely helpful in a wide scope of vision-based applications; along these lines content detection and recognition in regular scenes have turned out to be significant and dynamic research points in PC vision and report investigation. Traffic sign detection and recognition is a field of connected PC vision research worried about the programmed detection and grouping or recognition of traffic signs in scene pictures procured from a moving vehicle. Driving is an assignment dependent on visual data handling. The traffic signs characterize a visual language translated by drivers. Traffic signs convey much data important for effective driving; they portray current traffic circumstance, characterize option to proceed, preclude or grant certain headings. In this paper, talked about different detection and recognition schemes

SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection

Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects, 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.Comment: Accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS

Object Sub-Categorization and Common Framework Method using Iterative AdaBoost for Rapid Detection of Multiple Objects

Object detection and tracking in real time has numerous applications and benefits in various fields like survey, crime detection etc. The idea of gaining useful information from real time scenes on the roads is called as Traffic Scene Perception (TSP). TSP actually consists of three subtasks namely, detecting things of interest, recognizing the discovered objects and tracking of the moving objects. Normally the results obtained could be of value in object recognition and tracking, however the detection of a particular object of interest is of higher value in any real time scenario. The prevalent systems focus on developing unique detectors for each of the above-mentioned subtasks and they work upon utilizing different features. This obviously is time consuming and involves multiple redundant operations. Hence in this paper a common framework using the enhanced AdaBoost algorithm is proposed which will examine all dense characteristics only once thereby increasing the detection speed substantially. An object sub-categorization strategy is proposed to capture the intra-class variance of objects in order to boost generalisation performance even more. We use three detection applications to demonstrate the efficiency of the proposed framework: traffic sign detection, car detection, and bike detection. On numerous benchmark data sets, the proposed framework delivers competitive performance using state-of-the-art techniques

Fast detection of multiple objects in traffic scenes with a common detection framework

Traffic scene perception (TSP) aims to extract accurate real-time on-road environment information, which involves three phases: detection of objects of interest, recognition of detected objects, and tracking of objects in motion. Since recognition and tracking often rely on the results from detection, the ability to detect objects of interest effectively plays a crucial role in TSP. In this paper, we focus on three important classes of objects: traffic signs, cars, and cyclists. We propose to detect all the three important objects in a single learning-based detection framework. The proposed framework consists of a dense feature extractor and detectors of three important classes. Once the dense features have been extracted, these features are shared with all detectors. The advantage of using one common framework is that the detection speed is much faster, since all dense features need only to be evaluated once in the testing phase. In contrast, most previous works have designed specific detectors using different features for each of these three classes. To enhance the feature robustness to noises and image deformations, we introduce spatially pooled features as a part of aggregated channel features. In order to further improve the generalization performance, we propose an object subcategorization method as a means of capturing the intraclass variation of objects. We experimentally demonstrate the effectiveness and efficiency of the proposed framework in three detection applications: traffic sign detection, car detection, and cyclist detection. The proposed framework achieves the competitive performance with state-of-the-art approaches on several benchmark data sets.Qichang Hu, Sakrapee Paisitkriangkrai, Chunhua Shen, Anton van den Hengel, and Fatih Porikl

Unconstrained Road Sign Recognition

There are many types of road signs, each of which carries a different meaning and function: some signs regulate traffic, others indicate the state of the road or guide and warn drivers and pedestrians. Existent image-based road sign recognition systems work well under ideal conditions, but experience problems when the lighting conditions are poor or the signs are partially occluded. The aim of this research is to propose techniques to recognize road signs in a real outdoor environment, especially to deal with poor lighting and partially occluded road signs. To achieve this, hybrid segmentation and classification algorithms are proposed. In the first part of the thesis, we propose a hybrid dynamic threshold colour segmentation algorithm based on histogram analysis. A dynamic threshold is very important in road sign segmentation, since road sign colours may change throughout the day due to environmental conditions. In the second part, we propose a geometrical shape symmetry detection and reconstruction algorithm to detect and reconstruct the shape of the sign when it is partially occluded. This algorithm is robust to scale changes and rotations. The last part of this thesis deals with feature extraction and classification. We propose a hybrid feature vector based on histograms of oriented gradients, local binary patterns, and the scale-invariant feature transform. This vector is fed into a classifier that combines a Support Vector Machine (SVM) using a Random Forest and a hybrid SVM k-Nearest Neighbours (kNN) classifier. The overall method proposed in this thesis shows a high accuracy rate of 99.4% in ideal conditions, 98.6% in noisy and fading conditions, 98.4% in poor lighting conditions, and 92.5% for partially occluded road signs on the GRAMUAH traffic signs dataset

Primena inteligentnih sistema mašinske vizije autonomnog upravljanja železničkim vozilima

The railway is an important type of transport and has a significant economic impact on the industry and people's everyday life. Due to its capacities and complex infrastructure, it is necessary to work on its constant development and improvement. Railway automation requires the use of intelligent systems as a necessary part of an autonomous railway vehicle. As from the point of view of safe traffic, the existence of the object on the rail track and / or in its vicinity represents a potential obstacle to the railway traffic, and visibility has a very important role in correct and timely detection of the object on the railway infrastructure, a key element of autonomous railway vehicle is an obstacle detection system on the part of the railway infrastructure, in conditions of reduced visibility. The subject of scientific research of this doctoral dissertation is the application of intelligent machine vision systems in autonomous train operation. For the purpose of detecting obstacles on the part of the railway infrastructure in conditions of reduced visibility, a thermal imaging camera and a night vision system are integrated into the system, coupled with a developed advanced algorithm for image processing with artificial intelligence tools. In addition, the distance from the machine vision system to the detected object was estimated. The operation of the system was tested in a series of field experiments, at different locations, in different visibility conditions and weather conditions, through realistic scenarios