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Vehicle classification based on multiple fuzzy c-means clustering using dimensions and speed features
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- Javadi, Mohammad Saleh, 1986- (author)
- Blekinge Tekniska Högskola,Institutionen för matematik och naturvetenskap
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- Rameez, Muhammad, 1988- (author)
- Blekinge Tekniska Högskola,Institutionen för matematik och naturvetenskap
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- Dahl, Mattias (author)
- Blekinge Tekniska Högskola,Institutionen för matematik och naturvetenskap
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- (creator_code:org_t)
- Elsevier, 2018
- 2018
- English.
- In: Procedia Computer Science. - : Elsevier. ; , s. 1344-1350
- Conference paper (peer-reviewed)
Abstract
Subject headings
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- 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.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Keyword
- Vehicle classification
- Fuzzy c-means clustering
- Intelligent transportation systems
- Pattern recognition
Publication and Content Type
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- kon (subject category)
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