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Video-based Vehicle Detection and Classification in Heterogeneous Traffic Conditions using a Novel Kernel Classifier

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Title Video-based Vehicle Detection and Classification in Heterogeneous Traffic Conditions using a Novel Kernel Classifier
 
Creator MISHRA, PK
ATHIQ, M
NANDORIYA, A
CHAUDHURI, S
 
Subject Background model
Blob tracking
Heterogeneous traffic
Support vector machines classifier
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Description Vehicle classification in a traffic video is considered a difficult task due to similarity in appearances among different vehicles. This paper presents a real-time algorithm for detection and classification of different categories of vehicles in a heterogeneous traffic video. The processing of the video is done in four steps starting with camera calibration, vehicle detection, speed estimation, and classification. Vehicle detection is achieved by using background subtraction and blob tracking method. Speed of the detected vehicle is estimated by utilizing virtual start and stop line markers and calibration parameters. The vehicle classification is done by extracting multiple features of the detected vehicles which serve as input to a support vector machine based classifier. A histogram-based nonlinear kernel is used in the classifier. A combination of interest point detectors and low-level shape detectors as features was found to produce accurate and consistent results.
 
Publisher MEDKNOW PUBLICATIONS & MEDIA PVT LTD
 
Date 2014-10-15T15:41:27Z
2014-10-15T15:41:27Z
2013
 
Type Article
 
Identifier IETE JOURNAL OF RESEARCH, 59(5)541-550
http://dx.doi.org/10.4103/0377-2063.123760
http://dspace.library.iitb.ac.in/jspui/handle/100/15179
 
Language en