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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
Article

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.

Key words

Object detection Object tracking Feature extraction Remotely operated vehicle (ROV) 

CLC number

TP391 

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Notes

Acknowledgements

The non-governmental organization OCEANA and the team of the project IMPACT ‘Long-Term Effects of Continued Trawling on Deep-Water Muddy Ground’, financed within the scope of the European Union program EUROFLEETS, are gratefully acknowledged for the authorization to use the underwater video footage analyzed herein.

References

  1. Akbani R, Kwek S, Japkowicz N, 2004. Applying support vector machines to imbalanced datasets. Proc 15th European Conf on Machine Learning, p.39–50.  https://doi.org/10.1007/978-3-540-30115-8_7 Google Scholar
  2. Badekas E, Papamarkos N, 2005. Automatic evaluation of document binarization results. Proc 10th Iberoamerican Congress Conf on Progress in Patt Recognition, Image Analysis and Applications, p.1005–1014.  https://doi.org/10.1007/11578079_103 CrossRefGoogle Scholar
  3. Ben-Hur A, Weston J, 2010. 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, p.223–239.  https://doi.org/10.1007/978-1-60327-241-4_13 CrossRefGoogle Scholar
  4. Bernardin K, Stiefelhagen R, 2008. Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J Image Video Process, 2008:246309.  https://doi.org/10.1155/2008/246309 CrossRefGoogle Scholar
  5. Bouguet JY, 2000. Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the Algorithm. Intel Corporation Microprocessor Research Labs, Santa Clara, USA.Google Scholar
  6. Correia PL, Lau PY, Fonseca P, et al., 2007. Underwater video analysis for Norway lobster stock quantification using multiple visual attention features. Proc 15th European Signal Processing Conf, p.1764–1768.Google Scholar
  7. Denise S, 2007. Homework Helpers: Calculus (Homework Helpers). Career Press, Wayne.Google Scholar
  8. Fonseca P, Correia PL, Campos A, et al., 2008. Fishery-independent estimation of benthic species density—a novel approach applied to Norway lobster Nephrops norvegicus. Mar Ecol Prog Ser, 369:267–271.  https://doi.org/10.3354/meps076091 CrossRefGoogle Scholar
  9. Howard FG, 1989. The Norway lobster. Scott Fisher Inform Pamphl, No. 7.Google Scholar
  10. Johnsen S, Sosik H, 2004. Shedding light on light in the ocean. Ocean Mag, 43(2):1–5.Google Scholar
  11. Kuhn HW, 1955. The Hungarian method for the assignment problem. Nav Res Log Q, 2(1–2):83–97. https://doi.org/10.1002/nav.3800020109MathSciNetCrossRefzbMATHGoogle Scholar
  12. Lau PY, Correia PL, Fonseca P, et al., 2008. I2N2: a software for the classification of benthic habitats characteristics. Proc 16th European Signal Processing Conf, p.1–5.Google Scholar
  13. Lau PY, Correia PL, Fonseca P, et al., 2012. Estimating Norway lobster abundance from deep-water videos: an automatic approach. IET Image Process, 6(1):22–30.  https://doi.org/10.1049/iet-ipr.2009.0426 MathSciNetCrossRefGoogle Scholar
  14. Morello EB, Froglia C, Atkinson RJA, 2007. 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, 64(6):1116–1123.  https://doi.org/10.1093/icesjms/fsm082 Google Scholar
  15. Sardà F, Aguzzi J, 2012. A review of burrow counting as an alternative to other typical methods of assessment of Norway lobster populations. Rev Fish Biol Fisher, 22(2):409–422.  https://doi.org/10.1007/s11160-011-9242-6 CrossRefGoogle Scholar
  16. Sauvola J, Pietikäinen M, 2000. Adaptive document image binarization. Patt Recogn, 33(2):225–236.  https://doi.org/10.1016/S0031-3203(99)00055-2 CrossRefGoogle Scholar
  17. Shafait F, Keysers D, Breuel TM, 2008. Efficient implementation of local adaptive thresholding techniques using integral images. Proc SPIE, 6815:10.  https://doi.org/10.1117/12.767755 Google Scholar
  18. Sooknanan K, Doyle J, Wilson J, et al., 2013. Mosaics for burrow detection in underwater surveillance video. OCEANS, p.1–6.  https://doi.org/10.23919/OCEANS.2013.6741296 Google Scholar
  19. Struc V, Vesnicer B, Pavesic N, 2008. The phase-based Gabor fisher classifier and its application to face recognition under varying illumination conditions. Proc 2nd Int Conf on Signal Processing and Communication Systems, p.1–6.  https://doi.org/10.1109/ICSPCS.2008.4813663 Google Scholar
  20. Suzuki S, Be K, 1985. Topological structural analysis of digitized binary images by border following. Comput Vis Graph Image Process, 30(1):32–46.CrossRefzbMATHGoogle Scholar
  21. Tan CS, Lau PY, Low TJ, et al., 2014. Detection of marine species on underwater video images. Int Workshop on Advanced Image Technology, p.192–196.Google Scholar
  22. Tan CS, Lau PY, Correia PL, et al., 2015. A tracking scheme for Norway lobster and burrow abundance estimation in underwater video sequences. Proc Int Workshop on Advanced Image Technology.Google Scholar
  23. Yang CJ, Duraiswami R, Davis L, 2005. Fast multiple object tracking via a hierarchical particle filter. Proc IEEE Int Conf on Computer Vision, p.212–219.  https://doi.org/10.1109/ICCV.2005.95 Google Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Centre for Computing and Intelligent SystemsUniversiti Tunku Abdul RahmanKamparMalaysia
  2. 2.Instituto de TelecomunicaçõesInstituto Superior TécnicoLisbonPortugal
  3. 3.Instituto Português do Mar e da Atmosfera (IPMA), Divisão de Modelação e Gestão de Recursos da PescaLisbonPortugal
  4. 4.Centro de Ciências do Mar (CCMAR) - Campus de GambelasFaroPortugal

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