Title

END-TO-END NETWORKS FOR DETECTION AND TRACKING OF MICRO UNMANNED AERIAL VEHICLES

Abstract

Abstract

As the number of micro unmanned aerial vehicles (mUAV) increases,
several threats arise. Hence, there is a need for system that can detect
and track them. In this thesis, an end-to-end object detection model
based on convolutional neural networks for mUAV detection and a novel
end-to-end object tracking architecture are proposed. To solve the
scarce data problem for training the detection network, an algorithm for
creating an extensive artificial dataset by combining
background-subtracted real images is proposed. It has been shown that
the created dataset is adequate for training a well performing networks.

Keywords: Object Detection, Object Tracking, Convolutional Neural
Networks, Neural Turing Machine, Deep Learning

Biography:

Supervisor(s)

Supervisor(s)

CEMAL AKER

Date and Location

Date and Location

2018-09-05;10:00:00-A105

Category

Category

MSc_Thesis