This document summarizes a research paper that implements lane line detection in images and videos using the Hough transform and Gaussian smoothing. The methodology section outlines the steps taken, which include converting the image to grayscale, applying Gaussian smoothing for noise reduction, using Canny edge detection to extract edges, and applying the Hough transform to detect lane lines. Key algorithms discussed are Gaussian smoothing, Canny edge detection, Hough transformation, grayscale conversion, and defining a region of interest. The implementation section demonstrates applying these techniques to detect lane lines, including masking the image, edge detection, and identifying the lane lines.
LANE CHANGE DETECTION AND TRACKING FOR A SAFE-LANE APPROACH IN REAL TIME VISI...cscpconf
Image sequences recorded with cameras mounted in a moving vehicle provide information
about the vehicle’s environment which has to be analysed in order to really support the driver
in actual traffic situations. One type of information is the lane structure surrounding the vehicle.
Therefore, driver assistance functions which make explicit use of the lane structure represented
by lane borders and lane markings is to be analysed. Lane analysis is performed on the road
region to remove road pixels. Only lane markings are the interests for the lane detection
process. Once the lane boundaries are located, the possible edge pixels are scanned to
continuously obtain the lane model. The developed system can reduce the complexity of vision
data processing and meet the real time requirements.
LANE CHANGE DETECTION AND TRACKING FOR A SAFE-LANE APPROACH IN REAL TIME VISI...cscpconf
Image sequences recorded with cameras mounted in a moving vehicle provide information
about the vehicle’s environment which has to be analysed in order to really support the driver
in actual traffic situations. One type of information is the lane structure surrounding the vehicle.
Therefore, driver assistance functions which make explicit use of the lane structure represented
by lane borders and lane markings is to be analysed. Lane analysis is performed on the road
region to remove road pixels. Only lane markings are the interests for the lane detection
process. Once the lane boundaries are located, the possible edge pixels are scanned to
continuously obtain the lane model. The developed system can reduce the complexity of vision
data processing and meet the real time requirements.
Image Processing is any form of signal processing for which our input is an image, such as photographs or frames of videos and our output can be either an image or a set of characterstics related to the image
Google driverless car technical seminar report (.docx)gautham p
Google Driverless Car is the latest technology or innovation that is going to hit the market in the coming years.
This report is especially for mechanical engineering students.
It is a presentation for initial review of the project "Lane Detection". This project is useful for advanced driver assistance systems. We are developing this project by using computer vision. It includes gray scale conversion, noise reduction, canny edge detection, hough lane transform and some other user defined functions. The language we are using is python. Gray scale conversion converts the image from RGB format to gray. Since working with single colored channel image is much easier than working with three colored channel image. By using gaussian filter, noise reduction is performed. All the unwanted data, outliers, noisy data are removed. Simply the image is blurred. Next is canny edge detection, in this method edges present in the image are detected. And next region of interest is considered and hough lane transform is performed to get lanes on the road image.
Automatic Road Sign Recognition From VideoDr Wei Liu
Road signs provide important information for guiding, warning, or regulating the drivers’ behaviour in order to make driving safer and easier. The Road Sign Recognition (RSR) is a field of applied computer vision research concerned with the automatic detection and classification of traffic signs in traffic scene images acquired from a moving car. Pavement Management Services has developed the first truly spatially registered video system in Australia. The digital video system offers continuous, high resolution video capture of five different views along the roadway. In this paper a road sign recognition system (RS2) for the high resolution roadside video recorded by PMS system will be introduced. The recognition process of RS2 is divided into three distinct parts: detection and location, recognition and classification, and display and record for information of road signs. While lots of attempts at automated sign recognition were based on the detection of shape patterns, the proposed method for PMS Video detects road signs by recognising their patterns in color space. Based on the performance testing of proposed RS2 for the road video collected in state highway network, the proposed approach is found to be robust and fast for detection of most of road signs commonly found in New Zealand, including warning signs, information signs, regulatory signs, and street signs. The sign recognition results include the exact locations of the road sign, types of road sign, and the images containing the road sign detected, which can be presented in various format and be used in sign condition evaluation for asset management.
Image Processing is any form of signal processing for which our input is an image, such as photographs or frames of videos and our output can be either an image or a set of characterstics related to the image
Google driverless car technical seminar report (.docx)gautham p
Google Driverless Car is the latest technology or innovation that is going to hit the market in the coming years.
This report is especially for mechanical engineering students.
It is a presentation for initial review of the project "Lane Detection". This project is useful for advanced driver assistance systems. We are developing this project by using computer vision. It includes gray scale conversion, noise reduction, canny edge detection, hough lane transform and some other user defined functions. The language we are using is python. Gray scale conversion converts the image from RGB format to gray. Since working with single colored channel image is much easier than working with three colored channel image. By using gaussian filter, noise reduction is performed. All the unwanted data, outliers, noisy data are removed. Simply the image is blurred. Next is canny edge detection, in this method edges present in the image are detected. And next region of interest is considered and hough lane transform is performed to get lanes on the road image.
Automatic Road Sign Recognition From VideoDr Wei Liu
Road signs provide important information for guiding, warning, or regulating the drivers’ behaviour in order to make driving safer and easier. The Road Sign Recognition (RSR) is a field of applied computer vision research concerned with the automatic detection and classification of traffic signs in traffic scene images acquired from a moving car. Pavement Management Services has developed the first truly spatially registered video system in Australia. The digital video system offers continuous, high resolution video capture of five different views along the roadway. In this paper a road sign recognition system (RS2) for the high resolution roadside video recorded by PMS system will be introduced. The recognition process of RS2 is divided into three distinct parts: detection and location, recognition and classification, and display and record for information of road signs. While lots of attempts at automated sign recognition were based on the detection of shape patterns, the proposed method for PMS Video detects road signs by recognising their patterns in color space. Based on the performance testing of proposed RS2 for the road video collected in state highway network, the proposed approach is found to be robust and fast for detection of most of road signs commonly found in New Zealand, including warning signs, information signs, regulatory signs, and street signs. The sign recognition results include the exact locations of the road sign, types of road sign, and the images containing the road sign detected, which can be presented in various format and be used in sign condition evaluation for asset management.
Lane and Object Detection for Autonomous Vehicle using Advanced Computer VisionYogeshIJTSRD
The vision of this project is to develop lane and object detection in Autonomous Vehicle system to run efficiently in normal road condition and to eliminate the use of high cost Light based LiDAR system to implement high resolution cameras with advanced computer vision and deep learning technology to provide an Advanced Driver Assistance System ADAS . Detecting lane lines could be a crucial task for any self driving autonomous vehicle. Hence, this project was focused to identify lane lines on the road using OpenCV. The OpenCV tools such as colour selection, the region of interest selection, grey scaling, canny edge detection and perspective transformation are being employed. This project is modelled as an integration of two systems to solve the real time implementation problem in autonomous vehicles. The first part of the system is lane detection by advanced computer vision techniques to detect the lane lines to command the vehicle to stay inside the lane marking. The second part of the system is object detection and tracking is to detect and track the vehicle and pedestrians on the road to get a clear understanding of the environment to plan and generate a trajectory to navigate the autonomous vehicle safely to its destination without any crashes, this is done by a special deep learning method called transfer learning with Single Shot multibox Detection SSD algorithm and Mobile Net architecture. G. Monika | S. Bhavani | L. Azim Jahan Siana | N. Meenakshi "Lane and Object Detection for Autonomous Vehicle using Advanced Computer Vision" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd39952.pdf Paper URL: https://www.ijtsrd.com/engineering/electronics-and-communication-engineering/39952/lane-and-object-detection-for-autonomous-vehicle-using-advanced-computer-vision/g-monika
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Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
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