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Change detection in aerial images using a Kendall's TAU distance pattern correlation
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-6643-312X
2016 (English)In: PROCEEDINGS OF THE 2016 6TH EUROPEAN WORKSHOP ON VISUAL INFORMATION PROCESSING (EUVIP), IEEE, 2016Conference paper, Published paper (Refereed)
Abstract [en]

Change detection in aerial images is the core of many remote sensing applications to analyze the dynamics of a wide area on the ground. In this paper, a remote sensing method is proposed based on viewpoint transformation and a modified Kendall rank correlation measure to detect changes in oblique aerial images. First, the different viewpoints of the aerial images are compromised and then, a local pattern descriptor based on Kendall rank correlation coefficient is introduced. A new distance measure referred to as Kendall's Tau-d (Tau distance) coefficient is presented to determine the changed regions. The developed system is applied on oblique aerial images with very low aspect angles that obtained using an unmanned aerial vehicle in two different days with drastic change in illumination and weather conditions. The experimental results indicate the robustness of the proposed method to variant illumination, shadows and multiple viewpoints for change detection in aerial images.

Place, publisher, year, edition, pages
IEEE, 2016.
Keywords [en]
Aerial images, change detection, Kendall rank correlation, optical remote sensing
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:bth-13878DOI: 10.1109/EUVIP.2016.7764604ISI: 000391630800023ISBN: 978-1-5090-2781-1 (electronic)OAI: oai:DiVA.org:bth-13878DiVA, id: diva2:1071285
Conference
2016 6th European Workshop on Visual Information Processing (EUVIP), Marseille
Available from: 2017-02-03 Created: 2017-02-03 Last updated: 2018-10-24Bibliographically approved
In thesis
1. Computer Vision Algorithms for Intelligent Transportation Systems Applications
Open this publication in new window or tab >>Computer Vision Algorithms for Intelligent Transportation Systems Applications
2018 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In recent years, Intelligent Transportation Systems (ITS) have emerged as an efficient way of enhancing traffic flow, safety and management. These goals are realized by combining various technologies and analyzing the acquired data from vehicles and roadways. Among all ITS technologies, computer vision solutions have the advantages of high flexibility, easy maintenance and high price-performance ratio that make them very popular for transportation surveillance systems. However, computer vision solutions are demanding and challenging due to computational complexity, reliability, efficiency and accuracy among other aspects.

In this thesis, three transportation surveillance systems based on computer vision are presented. These systems are able to interpret the image data and extract the information about the presence, speed and class of vehicles, respectively. The image data in these proposed systems are acquired using Unmanned Aerial Vehicle (UAV) as a non-stationary source and roadside camera as a stationary one. The goal of these works is to enhance the general performance in accuracy and robustness of the systems with variant illumination and traffic conditions.

This is a compilation thesis in systems engineering consists of three parts. The red thread through each part is a transportation surveillance system. The first part presents a change detection system using aerial images of a cargo port. The extracted information shows how the space is utilized at various times for further management and development of the port. The proposed solution can be used at different viewpoints and illumination levels e.g. sunset. The method is able to transform the images taken from different viewpoints and match them together and then using a proposed adaptive local threshold to detect discrepancies between them. In the second part, a vision-based vehicle's speed estimation system is presented. The measured speeds are essential information for law enforcement as well as estimation of traffic flow at certain points on the road. The system employs several intrusion lines to extract the movement pattern of each vehicle (non-equidistant sampling) as an input feature to the proposed analytical model. In addition, other parameters such as camera sampling rate and distances between intrusion lines are also taken into account to address the uncertainty in the measurements and to obtain the probability density function of the vehicle's speed. In the third part, a vehicle classification system is provided to categorize vehicles into “private cars", “light trailers", “lorry or bus" and “heavy trailer". This information can be used by authorities for surveillance and development of the roads. The proposed system consists of multiple fuzzy c-means clusterings using input features of length, width and speed of each vehicle. The system has been constructed using prior knowledge of traffic regulations regarding each class of vehicle in order to enhance the classification performance.

Place, publisher, year, edition, pages
Karlshamn: Blekinge Tekniska Högskola, 2018
Series
Blekinge Institute of Technology Licentiate Dissertation Series, ISSN 1650-2140 ; 5
Keywords
computer vision, intelligent transportation systems (ITS), speed measurement, vehicle classification
National Category
Signal Processing Computer Vision and Robotics (Autonomous Systems) Other Computer and Information Science
Identifiers
urn:nbn:se:bth-17166 (URN)978-91-7295-359-8 (ISBN)
Presentation
2018-11-29, Blekinge Institute of technology, Karlshamn, 10:00 (English)
Opponent
Supervisors
Available from: 2018-10-25 Created: 2018-10-24 Last updated: 2018-12-17Bibliographically approved

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Publisher's full texthttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7764604&isnumber=7764575

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Javadi, Mohammad SalehDahl, MattiasPettersson, Mats

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