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 |
|