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This study describes a method for using a camera to automatically recognize the speed limits on speed-limit signs. This method consists of the following three processes: first (1) a method of detecting the speed-limit signs with a machine learning method utilizing the local binary pattern (LBP) feature quantities as information helpful for identification, then (2) an image processing method using...
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 this paper, we propose a framework that can prevent accidents due to careless or inattentive driving by providing the necessary traffic information to the driver. The proposed system complements the driver by providing the missed cognitive information regarding the traffic. The proposed system is divided into three parts. First, the system checks the condition of the driver in real time, and detects...
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,...
Road marking is a key visual cue for driving in structured environments like highways and urban roads. Road marking detection plays an important role in advanced driver assistant systems and autonomous driving. Robust road marking detection is challenging for the variation of road scenes, the degradation of the markings and the changes of the illumination. Traditional algorithms mainly use the grayscale...
Recently, detection and recognition of traffic panels and their textual information is studied increasingly to become the next working part of driver assistance systems and autonomous cars. These information are especially useful when other facilities fail to provide enough information about routes and places, like when Global Positioning System (GPS) gets blocked in high density urban areas. However,...
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...
Lane detection is one of the most challenging problems in machine vision and still has not been fully accomplished because of the highly sensitive nature of computer vision methods. Computer vision depends on various ambient factors. External illumination conditions, camera and captured image quality etc. effect machine vision performance. Lane detection faces all these challenges as well as those...
We present a robust real-time vision-based system for vehicle tracking and categorization, developed for traffic flow surveillance. We propose a robust segmentation algorithm that detects foreground pixels corresponding to moving vehicles. Experimental results based on four large datasets show that our method can count and classify vehicles with a high level of performance (more than 98%).
To deal with road accidents, especially accidents caused by trucks containing dangerous products, the possible solution is to control these vehicles' passage. We aim at developing a software technique confirming that all the entered engines inside a tunnel are securely quitted, to guarantee that no accidents, no breakdowns have occurred inside. To implement such solution, we identify the ingoing and...
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...
This paper introduces a dramatically novel traffic signs recognition (TSR) system that can perform traffic sign detection and tracking simultaneously. The proposed approach utilizes intensity images and the depth images, in parallel, to robustly detect and track traffic signs in real-time. Additionally, we suggest to supplement the ordinary traffic signs with the corresponding quick-response (QR)...
Recognition of driving-view image is the most important future issue in a smart tachograph. The intersection imaging recognition is one of the important issues in driving-view image recognition. In addition to using in the GPS satellite positioning correction in the metropolitan area, it can also be applied to the traffic lights imaging recognition in an intersection. With some samples analyzing,...
In this paper an automated vehicle detection and traffic density estimation algorithm has been developed and validated for very high resolution satellite video data. The algorithm is based on an adaptive background estimation procedure followed by a background subtraction at every video frame. The vehicle detection is performed through a further mathematical morphology and statistical analysis on...
Lane detection and tracking and departure warning systems are important components of Intelligent Transportation Systems. They have particularly attracted great interest from industry and academia. Many architectures and commercial systems have been proposed in the literature. In this paper, we discuss the design of such systems regarding the following stages: pre-processing, detection, and tracking...
When moving towards fully autonomous navigation, safety plays the most important role for both pedestrian and driver. This paper proposes a method to estimate the lane road region of interest based on the stopping typical distance of a vehicle required by the current speed of the vehicle. This was achieved by taking advantage of the difference in color of the road surface given by the lane marking...
For autonomous navigation the real-time processing is crucial. This paper proposes a method to detect the lane markings in real-time using the advantage of parallel processing. A region of interest is constrained by the current velocity of a vehicle. The segmentation was achieved by utilizing a difference in color between lane marking and road pavement. The overall process is divided into three steps...
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