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CLC number: TP391

On-line Access: 2018-10-05

Received: 2017-11-09

Revision Accepted: 2018-08-22

Crosschecked: 2018-08-23

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Citations:  Bibtex RefMan EndNote GB/T7714


Ching Soon Tan


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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.8 P.1042-1055


Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance

Author(s):  Ching Soon Tan, Phooi Yee Lau, Paulo L. Correia, Aida Campos

Affiliation(s):  Centre for Computing and Intelligent Systems, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia; more

Corresponding email(s):   laupy@utar.edu.my

Key Words:  Object detection, Object tracking, Feature extraction, Remotely operated vehicle (ROV)

Ching Soon Tan, Phooi Yee Lau, Paulo L. Correia, Aida Campos. Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(8): 1042-1055.

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%T Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance
%A Ching Soon Tan
%A Phooi Yee Lau
%A Paulo L. Correia
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T1 - Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance
A1 - Ching Soon Tan
A1 - Phooi Yee Lau
A1 - Paulo L. Correia
A1 - Aida Campos
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 19
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SP - 1042
EP - 1055
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PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1700720

Underwater imaging is being used increasingly by marine biologists as a means to assess the abundance of marine resources and their biodiversity. Previously, we developed the first automatic approach for estimating the abundance of Norway lobsters and counting their burrows in video sequences captured using a monochrome camera mounted on trawling gear. In this paper, an alternative framework is proposed and tested using deep-water video sequences acquired via a remotely operated vehicle. The proposed framework consists of four modules: (1) pre-processing, (2) object detection and classification, (3) object-tracking, and (4) quantification. Encouraging results were obtained from available test videos for the automatic video-based abundance estimation in comparison with manual counts by human experts (ground truth). For the available test set, the proposed system achieved 100% precision and recall for lobster counting, and around 83% precision and recall for burrow detection.




Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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