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In this paper, we propose an illumination invariant lane color recognition method. Most of the conventional lane color recognition methods suffer from various illumination changes. In the past, the HSV color space has been commonly used to tell white and the yellow road lines, because the HSV color space is a range of specific colors. However, it is known that accurate road line recognition is difficult...
Duckietown is an open, inexpensive and flexible platform for autonomy education and research. The platform comprises small autonomous vehicles (“Duckiebots”) built from off-the-shelf components, and cities (“Duckietowns”) complete with roads, signage, traffic lights, obstacles, and citizens (duckies) in need of transportation. The Duckietown platform offers a wide range of functionalities at a low...
In this paper, we have implemented and tested a system of detection and recognition of road signs. The approach taken in this work consists of two main modules: a sensor module, which is based on color segmentation and shape detection where we converted the images to the HSV color space, then labeled the detected regions and tested for their shape. A recognition module, Template Matching, whose role...
Single materials have colors which form straight lines in RGB space. However, in severe shadow cases, those lines do not intersect the origin, which is inconsistent with the description of most literature. This paper is concerned with the detection and correction of the offset between the intersection and origin. First, we analyze the reason for forming that offset via an optical imaging model. Second,...
Illumination changes such as shadows significantly affect the accuracy of various road detection methods, especially for vision-based approaches with an on-board monocular camera. To efficiently consider such illumination changes, we propose a PCA based technique, PCA-II, that finds the minimum projection space from an input RGB image, and then use the space as the illumination-invariant space for...
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...
Traffic Sign Detection and Recognition is an important component of intelligent transportation systems. It has captured the attention of the computer vision community for several decades. In this paper, we propose a new traffic sign detection and recognition approach consisting of color segmentation, shape classification and recognition stages. In the first stage, the image is segmented using look-up...
Road sign recognition plays an exigent role for easy, immune and suitable driving. In this paper, a road sign detection system is developed to automatically recognize Bengali road signs. The proposed method is based on HSV transformation along with a template matching technique to detect and recognize circular, triangular, rectangular and octagonal signs and it covers all existing Bengali road signs...
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,...
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...
This paper proposes a framework of segment-based free space estimation using plane normal vector with stereo vision. An image is divided into compact superpixels and each of them is viewed as a plane composed of the normal vector in disparity space. To deal with the variation of illumination and shading in real traffic scenes, we estimate depth information for the segmented stereo pair. The representative...
The purpose of this paper is to describe a new approach in road detection. This research uses two detection processes approaches: RGB histogram Filterization and Boundary Classifer, which is different from previous works on road detection. RGB Histogram Filterization processes the reading from the camera in greyscale form and afterward processes them by color segmentation. The last step for this process...
Color segmentation is a preliminary step in many application computer vision systems today, as the detection of human movement, recognition of road traffic signs and video intelligent. Detection stage performance is therefore closely linked to the results obtained from the color segmentation. Detection and recognition automatic road traffic signs are applied in the color spaces RGB, HSV, and HSI....
In this paper, we present a new approach for the detection and classification of real time road signs from video. The proposed system is composed of two processing stages: detection and classification signs. The system has detected road signs by color and shape feature of segmentation color in HSV color space, especially red and blue. The detected signs are then classified by method template matching,...
In this paper, we present a new approach for the detection and classification of real time road signs from video. The proposed system is composed of two processing stages: detection and classification signs. The system has detected road signs by color and shape feature of segmentation color in HSV color space, especially red and blue. The detected signs are then classified by method template matching,...
This paper presents a novel online unsupervised vision system for obstacle detection in field environments which detects many obstacles pathological to appearance- or structure-only obstacle detection systems. Robust obstacle detection in field environments is challenging as it is infeasible to train on all possible obstacles in all conditions, and many obstacles are camouflaged in their appearance...
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,...
Automatically finding paths is a crucial and challenging task in autonomous navigation systems. The task becomes more difficult in unstructured environments such as indoor or outdoor scenes with unmarked pedestrian lanes under severe illumination conditions, complex lane surface structures, and occlusion. This paper proposes a robust method for pedestrian lane detection in such unstructured environments...
This paper describes the initial design of a computer vision application to recognize regulatory traffic signs vertically installed on Colombian roads using machine learning. This application is conceived as a module of a driver assistance system under development, and an autonomous vehicle adapted to the local infrastructure. The application was trained and tested with official synthetic images provided...
In recent years, a lot of researches on traffic sign detection and recognition have been done. But most of them were tested under restricted conditions such as camera with high resolution and sensitivity, highway environment or road side having a lot of trees and very few distracting objects. In this paper, we present a fast and robust traffic sign detection system including two main stages: segmentation...
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