Marine object detection using background modelling and blob analysis

Zhou, Hailing, Llewellyn, Lyndon, Wei, Lei, Creighton, Douglas and Nahavandi, Saeid 2015, Marine object detection using background modelling and blob analysis, in SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics, IEEE, Piscataway, N.J., pp. 430-435, doi: 10.1109/SMC.2015.86.

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Title Marine object detection using background modelling and blob analysis
Author(s) Zhou, HailingORCID iD for Zhou, Hailing orcid.org/0000-0001-5009-4330
Llewellyn, Lyndon
Wei, LeiORCID iD for Wei, Lei orcid.org/0000-0001-8267-0283
Creighton, DouglasORCID iD for Creighton, Douglas orcid.org/0000-0002-9217-1231
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)
Conference location Hong Kong, China
Conference dates 9-12 Oct. 2015
Title of proceedings SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and Cybernetics
Publication date 2015
Start page 430
End page 435
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) Science & Technology
Technology
Computer Science, Cybernetics
Computer Science, Information Systems
Computer Science, Theory & Methods
Computer Science
Object detection
object recognition
jellyfish
sea snake
marine object
Summary 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.
ISBN 9781479986965
ISSN 1062-922X
Language eng
DOI 10.1109/SMC.2015.86
Field of Research 080106 Image Processing
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082437

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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