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In this paper, we propose a multi-cue fusion approach to detect the road boundary using stereo vision, which fits road boundary with a few edge points. Firstly boundary areas are determined in accordance with the normal vector information. Based on the cues of normal vector, height and color in the boundary area, three Bayes models are established respectively. Then the Naive Bayes framework could...
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...
Road pavement structure analysis provides important information for road maintenance and renovation planning. Image processing methods applying to cross-section of the asphalt pavement can provide valuable information about the pavement structure and its quality. The paper presents a method to analyze a picture of a cross-section sample, taking into account aggregates size. The features that have...
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 additional information approaches, multiple image approaches and the single-image approaches are some of the techniques present for the removal of the haze. The first two methods are expensive one and has high computational complexity. Recently single image approach is used for this de-hazing process because of its flexibility and low cost. Bi-orthogonal wavelet transform is used to obtain the...
This paper presents a new algorithm for the detection and classification of real-time road traffic signs in the video. Our system is able to detect and classify triangular, circular, and octagonal signs of red and blue colors. The proposed system operates into two processing steps: (1) detection and (2) classification road signs. The system has detected candidate regions as Maximally Stable Extremal...
Road Sign Detection (RSD) is becoming a major goal of the safety Advanced Driving Assistance Systems (ADAS). Automotive research area share many publications based various techniques used to detect and classify signs. This paper provides a hardware detection-based correlation architecture using Xilinx System Generator (XSG). This proposed architecture outsets with pre-processing step: RGB to YCrCb...
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...
This paper presents a spatial-related traffic sign inspection process for sign type, position, and placement using mobile laser scanning (MLS) data acquired by a RIEGL VMX-450 system and presents its potential for traffic sign inventory applications. First, the paper describes an algorithm for traffic sign detection in complicated road scenes based on the retroreflectivity properties of 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...
This paper presents a design and implementation of the real-time traffic sign detection and recognition system based FriendlyARM Tiny4412 board. We develop an algorithm for detecting and recognizing the traffic signs in Vietnam with real-time processing capability and high accuracy. To achieve these objectives, we employ three main techniques consisting of traffic sign extraction based on chromatic...
In article the problem of classification of the recognizable spatial objects in geographic information systems is considered. Classification by such types of the spatial distributed objects as the imposed objects, extended objects, objects with gaps and objects with “holes” is executed. Signs of the spatial distributed objects of the specified types are given.
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%).
Computer vision based road detection is an indispensable and challenging task in many real-world applications such as obstacle detection in autonomous driving. Low-level image features (e.g., color and texture) and pre-trained models are commonly used for this task. In this paper, we propose a simple yet effective approach to detect roads from a single image, which avoids the supervised model training...
We explore an approach to use simple classification models to solve complex problems by partitioning the input domain into smaller regions that are more amenable to the classifier. For this purpose weinvestigate two variants of partitioning based on energy, as measured by the variance. We argue that restricting the energy of the input domain limits the complexity of the problem. Therefore, our method...
Free space and on-road obstacle detection is one of the key functions for the implementation of the vision-based intelligent vehicle and robot navigation system. Stereo vision-based algorithm for this task is more realistic and precious compared with radar or lidar-based algorithms. In addition, accurate estimation results can indicate the current and approaching conditions in the complex traffic...
Given a set of points in two dimensional space, a minimum radius, a minimum log likelihood ratio and a significance threshold, Geographically Robust Hotspot Detection (GRHD) finds hotspot areas where the concentration of points inside is significantly high. The GRHD problem is societally important for many applications including environmental criminology, epidemiology, etc. GRHD is computationally...
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...
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