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Marine object detection using background modelling and blob analysis

Version 2 2024-06-04, 00:10
Version 1 2016-03-23, 15:15
conference contribution
posted on 2024-06-04, 00:10 authored by H Zhou, L Llewellyn, Lei WeiLei Wei, Douglas CreightonDouglas Creighton, S Nahavandi
Monitoring marine object is important for understanding the marine ecosystem and evaluating impacts on different environmental changes. One prerequisite of monitoring is to identify targets of interest. Traditionally, the target objects are recognized by trained scientists through towed nets and human observation, which cause much cost and risk to operators and creatures. In comparison, a noninvasive way via setting up a camera and seeking objects in images is more promising. In this paper, a novel technique of object detection in images is presented, which is applicable to generic objects. A robust background modelling algorithm is proposed to extract foregrounds and then blob features are introduced to classify foregrounds. Particular marine objects, box jellyfish and sea snake, are successfully detected in our work. Experiments conducted on image datasets collected by the Australian Institute of Marine Science (AIMS) demonstrate the effectiveness of the proposed technique.

History

Pagination

430-435

Location

Hong Kong, China

Start date

2015-10-09

End date

2015-10-12

ISSN

1062-922X

ISBN-13

9781479986965

Language

eng

Publication classification

E Conference publication, E1 Full written paper - refereed

Copyright notice

2015, IEEE

Title of proceedings

SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics

Event

IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)

Publisher

IEEE

Place of publication

Piscataway, N.J.

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