Visual Localization in Aerial Imagery using Convolution Neural Network

dc.contributor.author	TAHIR SHAHZAD, Supervised By Dr Hasan Sajid
dc.date.accessioned	2020-11-05T09:51:05Z
dc.date.available	2020-11-05T09:51:05Z
dc.date.issued	2019
dc.identifier.uri	http://10.250.8.41:8080/xmlui/handle/123456789/10238
dc.description.abstract	The purpose of this research is to propose a novel approach using deep neural networks to estimate the position and orientation of aerial vehicle using vision sensor only. We will create and contribute an annotated dataset for visual odometry in aerial images. It will provide an alternative solution to locations where GPS is not working accurately. A novel DNN will be proposed and trained on data set that will cover diversity of land areas including cities, villages, forests, deserts, lakes, farms.	en_US
dc.language.iso	en_US	en_US
dc.publisher	SMME-NUST	en_US
dc.relation.ispartofseries	SMME-TH-416;
dc.subject	GPS Denied Environment, Travelling Distance Estimation, Convolutional Neural Networks, Deep Neural Network	en_US
dc.title	Visual Localization in Aerial Imagery using Convolution Neural Network	en_US
dc.type	Thesis	en_US