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The traffic sign detection and recognition is an integral part of Advanced Driver Assistance System (ADAS). Traffic signs provide information about the traffic rules, road conditions and route directions and assist the drivers for better and safe driving. Traffic sign detection and recognition system has two main stages: The first stage involves the traffic sign localization and the second stage classifies...
Automatic traffic sign detection and recognition is a field of computer vision which is very important aspect for advanced driver support system. This paper proposes a framework that will detect and classify different types of traffic signs from images. The technique consists of two main modules: road sign detection, and classification and recognition. In the first step, colour space conversion, colour...
Detecting roads using monocular vision is a very challenging task as the detection algorithm must be able to deal with complex real-road scenes. In this paper, we describe an algorithm for general path segmentation. There are three main technical contributions of the approach. First, a path segmentation framework is presented, which formulates road detection as a Bayesian posteriori estimation problem...
In the field of intelligent vehicle systems (IVS), color and edge of lane markings are important features for vision-based applications. This paper proposes a method to detect lane marking based on a fusion approach which combine color and edge lane marking information. Firstly, by knowing the vehicle speed the road surface region of interest is extracted using the typical stopping distance. Secondly,...
The traffic sign detection and recognition is necessary for the safety and proper navigation of drivers. Intelligent driver assistance systems have great potential in emerging technologies. This paper presents an efficient algorithm which detects the traffic sign from video based on colour and shape information. Then the auto associative neural networks are performed to recognise the traffic signs...
Traffic signs serve important functions on the road. Drivers can easily determine their directions and vehicle speeds by paying attention to traffic signs. However, it is only natural that sometimes drivers misjudge the position and meaning of traffic signs that they ignore them and in the worst case scenario, got involved in accidents. Therefore, technological improvements allow the development of...
Number plate recognition has been used widely for access control, congestion control, vehicle management, security control and vehicle behavior monitoring system. This study discusses the importance of number plate recognition and its corresponding application in different countries. Various methods for recognizing number plates are reviewed. Most of the systems are able to deliver good recognition...
To develop a safe Intelligent Transportation System (ITS) while driving on unpredictable curves or road regions, high precision road segmentation and cover level of forward view estimation for drivers is necessary. Cover level of forward view is defined as the level of difficulty in predicting the dangerousness of road edge or incoming object near road edge especially at a curve due to the obstacles...
Our aim is to automatically detect the road borders in a road scene image. This is useful to many road scenes analysis applications, in both fields of vehicle guidance and civil engineering. Difficulties arise because pavements are often heterogeneous and because illumination variations often occur in outdoor scenes. Some vehicle navigation projects use colour images for road borders detection [1,...
Driving vehicles under poor illumination and night conditions is stressful for drivers since co-vehicles that share the same road cannot easily be detected. The existing night vision solutions attempt to use enhancement algorithms or high cost thermal sensors. The enhancement techniques in the literature for night vision are complex and require costly processing hardware. We propose a low cost alternative...
This paper presents an approach for pixel-wise object segmentation for road scenes based on the integration of a color image and an aligned 3D point cloud. In light of the advantage of range information in object discovery, we first produce initial object hypotheses by clustering the sparse 3D point cloud. The image pixels registered to the clustered 3D points are taken as samples to learn each object's...
Text detection and recognition in images taken in uncontrolled environments still remains a challenge in computer vision. This paper presents a method to extract the text depicted in road panels in street view images as an application to Intelligent Transportation Systems (ITS). It applies a text detection algorithm to the whole image together with a panel detection method to strengthen the detection...
Automatic road-signs recognition is becoming a part of Driver Assisting Systems which role is to increase safety and driving comfort. This paper presents an efficient approach for detecting and recognizing road sign in traffic scene images acquired from a moving vehicle. The developed road sign recognition system is divided into two stages: detection stage to localize signs from a whole image, and...
This paper puts forward a new shadow defects segmentation method based on YCbCr color space. First, Mean Shift smoothing method is used to smooth the image pixels, and the motion area which includes the vehicle and the shadow defect is selected by binary discrete wavelet transforms after Mean Shift smoothing, and then the original data of the shadow defect according to the characteristics of the occurrence...
In this paper, we proposed a robust lane detection method. This method uses the globalized probability of boundary (gPb) algorithm as boundary detector and non-unique B-spline (NUBS) as the road model. The gPb algorithm combines the local information, like brightness, color and texture features, with global information derived from spectral partitioning and is robust against shadow, and illumination...
Road detection is the most fundamental part of autonomous vehicles. Noises caused by shadows and vehicles on the road have a great negative impact on the road detection. Our method improves the performance under the noisy environment by taking advantage of color information to determine the road curvature. A novel geometrical method is proposed in this paper to select the most matched curvature. A...
Considering the widespread problems of road transport, approach of the paper is a system to automatically control the roads by using images from satellite in night and day. Although no coherent system with appropriate performance has been yet introduced to achieve this goal, some methods has been proposed to estimate the road or recognize objects on the road, which have been more based on thresholding...
In this paper, we propose a system that automatically detects and recognizes road signs found in the United States, in real time or close to real-time. The proposed system has application to intelligent autonomous unmanned vehicles for urban surveillance and rescue. It is a multi-layered hierarchical scheme composed of 3 parts: road sign color segmentation, shape recognition, and classification. The...
Road detection is a vital task for the development of autonomous vehicles. The knowledge of the free road surface ahead of the target vehicle can be used for autonomous driving, road departure warning, as well as to support advanced driver assistance systems like vehicle or pedestrian detection. Using vision to detect the road has several advantages in front of other sensors: richness of features,...
In this paper we describe a novel approach to autonomous dirt road following. The algorithm is able to recognize highly curved roads in cluttered color images quite often appearing in offroad scenarios. To cope with large curvatures we apply gaze control and model the road using two different clothoid segments. A Particle Filter incorporating edge and color intensity information is used to simultaneously...
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