Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Vehicle classification based on multiple fuzzy c-means clustering using dimensions and speed features
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.
Blekinge Institute of Technology, Faculty of Engineering, Department of Mathematics and Natural Sciences.ORCID iD: 0000-0002-6643-312x
2018 (English)In: Procedia Computer Science, Elsevier, 2018, Vol. 126, p. 7p. 1344-1350Conference paper, Published paper (Refereed)
Abstract [en]

Vehicle classification has a significant use in traffic surveillance and management. There are many methods proposed to accomplish this task using variety of sensorS. In this paper, a method based on fuzzy c-means (FCM) clustering is introduced that uses dimensions and speed features of each vehicle. This method exploits the distinction in dimensions features and traffic regulations for each class of vehicles by using multiple FCM clusterings and initializing the partition matrices of the respective classifierS. The experimental results demonstrate that the proposed approach is successful in clustering vehicles from different classes with similar appearanceS. In addition, it is fast and efficient for big data analysiS.

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 126, p. 7p. 1344-1350
Series
Procedia Computer Science, ISSN 1877-0509
Keywords [en]
Vehicle classification, Fuzzy c-means clustering, Intelligent transportation systems, Pattern recognition
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:bth-17165DOI: 10.1016/j.procS.2018.08.085OAI: oai:DiVA.org:bth-17165DiVA, id: diva2:1258020
Conference
22nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2018), Belgrade
Note

open access

Available from: 2018-10-23 Created: 2018-10-23 Last updated: 2018-11-29Bibliographically 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

Open Access in DiVA

fulltext(1121 kB)117 downloads
File information
File name FULLTEXT01.pdfFile size 1121 kBChecksum SHA-512
21cc350e4f25b9848e7d8a6f8b513a0963bdd760f512526610d08d806ffead5a7c5b8e72c25fdc00c758707c64be385f591f3cdbdf446a90dd6d18efca844cb0
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Javadi, Mohammad SalehRameez, MuhammadDahl, MattiasPettersson, Mats

Search in DiVA

By author/editor
Javadi, Mohammad SalehRameez, MuhammadDahl, MattiasPettersson, Mats
By organisation
Department of Mathematics and Natural Sciences
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 117 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 122 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf