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

Ching Soon TAN; Phooi Yee LAU; Paulo L. CORREIA; Aida CAMPOS

doi:10.1631/FITEE.1700720

Your Location：

Home >

Browse articles >

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

Special Issue on Marine Information Technology | Updated：2022-05-19

- Automatic analysis of deep-water remotely operated vehicle footage for estimation of Norway lobster abundance
- 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
- Frontiers of Information Technology & Electronic Engineering Vol. 19, Issue 8, Pages: 1042-1055(2018)
- Affiliations：
  
  Centre for Computing and Intelligent Systems, Universiti Tunku Abdul Rahman, Kampar 31900, Malaysia
  Instituto de Telecomunicações, Instituto Superior Técnico, Av. Rovisco Pais, 1, Lisbon 1049-001, Portugal
  Instituto Português do Mar e da Atmosfera (IPMA), Divisão de Modelação e Gestão de Recursos da Pesca, Lisbon 1749-077, Portugal
  Centro de Ciências do Mar (CCMAR) - Campus de Gambelas, Faro 8005-139, Portugal
- Author bio：
  
  Phooi Yee LAU, E-mail: laupy@utar.edu.my
- Funds：
- DOI：10.1631/FITEE.1700720
  CLC： TP391
- Received：09 November 2017，
  
  Revised：2018-;8-23，
  
  Published：2018-08
- Accepted：
Scan QR Code
Ching Soon TAN, Phooi Yee LAU, Paulo L. CORREIA, et al. 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.
DOI：

Ching Soon TAN, Phooi Yee LAU, Paulo L. CORREIA, et al. 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. DOI： 10.1631/FITEE.1700720.

摘要

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%。

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) preprocessing

(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.

关键词

Keywords

references

R Akbani , , , S Kwek , , , N Japkowicz . . Applying support vector machines to imbalanced datasets . . Proc 15 th European Conf on Machine Learning , , 2004 . . p.39 - - 50 . . DOI: 10.1007/978-3-540-30115-8_7 http://doi.org/10.1007/978-3-540-30115-8_7 . .

E Badekas , , , N Papamarkos . . Automatic evaluation of document binarization results . . Proc 10 th Iberoamerican Congress Conf on Progress in Patt Recognition, Image Analysis and Applications , , 2005 . . p.1005 - - 1014 . . DOI: 10.1007/11578079_103 http://doi.org/10.1007/11578079_103 . .

A Ben-Hur , , , J Weston . . A user's guide to support vector machines . . In: Carugo O, Eisenhaber F (Eds.), Data Mining Techniques for the Life Sciences. Humana Press, New York , , 2010 . . p.223 - - 239 . . DOI: 10.1007/978-1-60327-241-4_13 http://doi.org/10.1007/978-1-60327-241-4_13 . .

K Bernardin , , , R Stiefelhagen . . Evaluating multiple object tracking performance: the CLEAR MOT metrics . . EURASIP J Image Video Process , , 2008 . . 2008 246309 DOI: 10.1155/2008/246309 http://doi.org/10.1155/2008/246309 . .

JY Bouguet . . Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the Algorithm . . Intel Corporation Microprocessor Research Labs, Santa Clara, USA , , 2000 . . .

PL Correia , , , PY Lau , , , P Fonseca , , , 等 . . Underwater video analysis for Norway lobster stock quantification using multiple visual attention features . . Proc 15 th European Signal Processing Conf , , 2007 . . p.1764 - - 1768 . . . .

S Denise . . Homework Helpers: Calculus (Homework Helpers) . . Career Press, Wayne , , 2007 . . .

P Fonseca , , , PL Correia , , , A Campos , , , 等 . . Fishery-independent estimation of benthic species density—a novel approach applied to Norway lobster Nephrops norvegicus . . Mar Ecol Prog Ser , , 2008 . . 369 267 - - 271 . . DOI: 10.3354/meps076091 http://doi.org/10.3354/meps076091 . .

FG Howard . . The Norway lobster . . Scott Fisher Inform Pamphl , , 1989 . . No. 7 . .

S Johnsen , , , H Sosik . . Shedding light on light in the ocean . . Ocean Mag , , 2004 . . 43 ( ( 2 ): ): 1 - - 5 . . . .

HW Kuhn . . The Hungarian method for the assignment problem . . Nav Res Log Q , , 1955 . . 2 ( ( 1-2 ): ): 83 - - 97 . . DOI: 10.1002/nav.3800020109CrossRefzbMATH http://doi.org/10.1002/nav.3800020109CrossRefzbMATH . .

PY Lau , , , PL Correia , , , P Fonseca , , , 等 . . I2N2: a software for the classification of benthic habitats characteristics . . Proc 16 th European Signal Processing Conf , , 2008 . . p.1 - - 5 . . . .

PY Lau , , , PL Correia , , , P Fonseca , , , 等 . . Estimating Norway lobster abundance from deep-water videos: an automatic approach . . IET Image Process , , 2012 . . 6 ( ( 1 ): ): 22 - - 30 . . DOI: 10.1049/iet-ipr.2009.0426 http://doi.org/10.1049/iet-ipr.2009.0426 . .

EB Morello , , , C Froglia , , , RJA Atkinson . . Underwater television as a fishery-independent method for stock assessment of Norway lobster ( Nephrops norvegicus ) in the central Adriatic Sea (Italy) . . ICES J Mar Sci , , 2007 . . 64 ( ( 6 ): ): 1116 - - 1123 . . DOI: 10.1093/icesjms/fsm082 http://doi.org/10.1093/icesjms/fsm082 . .

F Sardà , , , J Aguzzi . . A review of burrow counting as an alternative to other typical methods of assessment of Norway lobster populations . . Rev Fish Biol Fisher , , 2012 . . 22 ( ( 2 ): ): 409 - - 422 . . DOI: 10.1007/s11160-011-9242-6 http://doi.org/10.1007/s11160-011-9242-6 . .

J Sauvola , , , M Pietikäinen . . Adaptive document image binarization . . Patt Recogn , , 2000 . . 33 ( ( 2 ): ): 225 - - 236 . . DOI: 10.1016/S0031-3203(99)00055-2 http://doi.org/10.1016/S0031-3203(99)00055-2 . .

F Shafait , , , D Keysers , , , TM Breuel . . Efficient implementation of local adaptive thresholding techniques using integral images . . Proc SPIE , , 2008 . . 6815 10 DOI: 10.1117/12.767755 http://doi.org/10.1117/12.767755 . .

K Sooknanan , , , J Doyle , , , J Wilson , , , 等 . . Mosaics for burrow detection in underwater surveillance video . . OCEANS , , 2013 . . p.1 - - 6 . . DOI: 10.23919/OCEANS.2013.6741296 http://doi.org/10.23919/OCEANS.2013.6741296 . .

V Struc , , , B Vesnicer , , , N Pavesic . . The phase-based Gabor fisher classifier and its application to face recognition under varying illumination conditions . . Proc 2 nd Int Conf on Signal Processing and Communication Systems , , 2008 . . p.1 - - 6 . . DOI: 10.1109/ICSPCS.2008.4813663 http://doi.org/10.1109/ICSPCS.2008.4813663 . .

S Suzuki , , , K Be . . Topological structural analysis of digitized binary images by border following . . Comput Vis Graph Image Process , , 1985 . . 30 ( ( 1 ): ): 32 - - 46 . . . .

CS Tan , , , PY Lau , , , TJ Low , , , 等 . . Detection of marine species on underwater video images . . Int Workshop on Advanced Image Technology , , 2014 . . p.192 - - 196 . . . .

CS Tan , , , PY Lau , , , PL Correia , , , 等 . . A tracking scheme for Norway lobster and burrow abundance estimation in underwater video sequences . . Proc Int Workshop on Advanced Image Technology , , 2015 . . .

CJ Yang , , , R Duraiswami , , , L Davis . . Fast multiple object tracking via a hierarchical particle filter . . Proc IEEE Int Conf on Computer Vision , , 2005 . . p.212 - - 219 . . DOI: 10.1109/ICCV.2005.95 http://doi.org/10.1109/ICCV.2005.95 . .

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

S3Det: a fast object detector for remote sensing images based on artificial to spiking neural network conversion

UI layers merger: merging UI layers via visual learning and boundary prior

A modified YOLOv4 detection method for a vision-based underwater garbage cleaning robot^&

Classification of EEG-based single-trial motor imagery tasks using a B-CSP method for BCI

Automatic malware classification and new malware detection using machine learning

Related Author

Mingqian SUN

Xiaofeng QI

Yanzhao GAO

Guangwei XIE

Fan ZHANG

Li CHEN

Lingyun SUN

Yunnong CHEN

Related Institution

School of Cyber Science and Engineering, Southeast University

Computation and Artificial Intelligence Innovative College, Fudan University

National Digital Switching System Engineering & Technological R&D Center

Singapore Innovation and AI Joint Research Lab

Alibaba Group

Chat

Address：Zhejiang University Press, 148 Tianmushan Road, Hangzhou, China Postal code：310028
Tel：+86-571-88273162 Email：fitee@zju.edu.cn
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰