Moving ship detection for unmanned aerial vehicle using atom processor for camera vision

Ship detection has become an important approach for security and surveillance system in a maritime environment. By upgrading the ability of the detection module by using Unmanned Aerial Vehicle (UAV) will increase the coverage or limitation in operation module especially for navy purposes. UAV has p...

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Bibliographic Details
Main Author: Muhd. Yusof, Muhd. Firdaus
Format: Thesis
Language:English
Published: 2015
Subjects:
Online Access:http://eprints.utm.my/id/eprint/50674/25/MuhdFirdausMuhdMFKE2015.pdf
http://eprints.utm.my/id/eprint/50674/
http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85479
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Summary:Ship detection has become an important approach for security and surveillance system in a maritime environment. By upgrading the ability of the detection module by using Unmanned Aerial Vehicle (UAV) will increase the coverage or limitation in operation module especially for navy purposes. UAV has plays an important role in mobile aerial surveillance for military as well as civilian application. UAV is used to get an overview from bird’s eye view of locating and tracking an interested object in most operations. Unfortunately, in previous application the video processing and UAV control usually being done under ground control station which using wireless communication network to communicate among each other. It is not really practical by relying on video streaming via radio link as execution platform cause it may produced a delayed or blurred video images which will degrade detection performance. Maritime environment which consist robust environment and dynamic scenes gives a huge challenges for automatic object detection. This thesis presents the development of real-time moving ship detection in embedded platform using Inforce computing board which consist Intel Atom processor. Major part of this work is to validate the algorithm applied to be able to detect a moving ship and remove a camera motion that affect the detection part. The algorithm is tested offline using pre-captured videos from top view as similar from video captured from UAV. Experimental result shows that the algorithm applied are able to detect a moving ship but the performance are dependent on number of corners and skipping rate of frames used in the system. Thus, the algorithm used in the system is suitable for real-time ship moving detection system in UAV application.