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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

Cited: 0

Clicked: 3614

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Ching Soon Tan

http://orcid.org/0000-0002-6329-4558

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

http://doi.org/10.1631/FITEE.1700720


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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author="Ching Soon Tan, Phooi Yee Lau, Paulo L. Correia, Aida Campos",
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publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1700720"
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%A Paulo L. Correia
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A1 - Aida Campos
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Abstract: 
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.

深水遥控潜水器影片自动分析在挪威龙虾丰度估算中的应用

概要:水下成像技术越来越多地被海洋生物学家应用于海洋资源和生物多样性的丰度评估。之前,我们开发了挪威龙虾丰度测算方法,并利用安装在拖网上的黑白摄像机采集的视频序列对龙虾洞进行计数。在本文中,我们提出一种替代架构,并利用遥控潜水器采集的深水视频序列对该架构进行测试。该架构由以下4个模块组成:(1)预处理;(2)目标检测与分类;(3)目标追踪;(4)量化。在可用的测试视频中,对基于视频的自动丰度估算方法进行测试,并与专家人工计数结果(地表实值)比对,得到了令人鼓舞的结果。在可用的测试集中,所提出的系统在龙虾计数上的查准率和查全率达到100%,在龙虾洞计数上的查准率和查全率达到83%。

关键词:目标检测;目标追踪;特征提取;遥控潜水器

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

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