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Detection of vehicles using a cascaded classifier in comparison to a artificial neural network

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dc.contributor.author Fernando, S
dc.contributor.author Udawatta, L
dc.date.accessioned 2013-10-21T02:13:00Z
dc.date.available 2013-10-21T02:13:00Z
dc.date.issued 2009
dc.identifier.uri http://dl.lib.mrt.ac.lk/handle/123/8296
dc.description.abstract This paper describes the comparison of accuracy and performance of two machine learning approaches for visual object detection and tracking vehicles. The first is a neural network based approach. The classification was carried out with a multilayer feed forward neural network. The second approach is based on boosting It works by sequentially applying a classification algorithm to reweighed versions of the training data, followed by taking a weighted majority vote of the sequence of classifiers thus produced.
dc.language en
dc.title Detection of vehicles using a cascaded classifier in comparison to a artificial neural network
dc.type Conference-Extended-Abstract
dc.identifier.year 2009
dc.identifier.conference Research for Industry
dc.identifier.place Faculty of Engineering, University of Moratuwa
dc.identifier.pgnos pp. 107-108
dc.identifier.proceeding 15th Annual symposium on Research and Industry


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