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Journal of Toxicology Volume 2018 ,2018-04-03
Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress
Review Article
Chunlei Xia 1 Longwen Fu 1 Zuoyi Liu 2 Hui Liu 1 Lingxin Chen 1 Yuedan Liu 2
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DOI:10.1155/2018/2591924
Received 2017-07-14, accepted for publication 2018-02-08, Published 2018-02-08
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摘要

Video tracking based biological early warning system achieved a great progress with advanced computer vision and machine learning methods. Ability of video tracking of multiple biological organisms has been largely improved in recent years. Video based behavioral monitoring has become a common tool for acquiring quantified behavioral data for aquatic risk assessment. Investigation of behavioral responses under chemical and environmental stress has been boosted by rapidly developed machine learning and artificial intelligence. In this paper, we introduce the fundamental of video tracking and present the pioneer works in precise tracking of a group of individuals in 2D and 3D space. Technical and practical issues suffered in video tracking are explained. Subsequently, the toxic analysis based on fish behavioral data is summarized. Frequently used computational methods and machine learning are explained with their applications in aquatic toxicity detection and abnormal pattern analysis. Finally, advantages of recent developed deep learning approach in toxic prediction are presented.

授权许可

Copyright © 2018 Chunlei Xia et al. 2018
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

图表

Separating attached fish images using morphological operations [19].

Fitting individual fishes from occlusions.

Procedure of extracting individuals from occlusions using clustering and ellipse fitting [20].

Calculation of “Fingerprint” features [21].

Fish tracking by detecting fish head region [23].

Individual fishes represented by deformable models (yellow contour) and their skeletons (green line) [24].

A fish represented by a chain of rectangles [26].

Structures of 3D observation systems.

Structures of 3D observation systems.

Structures of 3D observation systems.

Structures of 3D observation systems.

Rectified views of stereo camera.

通讯作者

1. Hui Liu.Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China, cas.cn.Huiliu@yic.ac.cn
2. Yuedan Liu.The Key Laboratory of Water and Air Pollution Control of Guangdong Province, South China Institute of Environmental Sciences, MEP, Guangzhou 510065, China, scies.org.liuyuedan@scies.org

推荐引用方式

Chunlei Xia,Longwen Fu,Zuoyi Liu,Hui Liu,Lingxin Chen,Yuedan Liu. Aquatic Toxic Analysis by Monitoring Fish Behavior Using Computer Vision: A Recent Progress. Journal of Toxicology ,Vol.2018(2018)

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参考文献
[1] K. Nimkerdphol, M. Nakagawa. (2008). Effect of sodium hypochlorite on zebrafish swimming behavior estimated by fractal dimension analysis. Journal of Bioscience and Bioengineering.105(5):486-492. DOI: 10.1016/j.aquatox.2004.11.002.
[2] S. H. Wang, X. E. Cheng, Z. M. Qian, Y. Liu. et al.(2016). Automated Planar Tracking the Waving Bodies of Multiple Zebrafish Swimming in Shallow Water. PloS one.11(4). DOI: 10.1016/j.aquatox.2004.11.002.
[3] A. Mayr, G. Klambauer, T. Unterthiner, S. Hochreiter. et al.(2016). DeepTox: toxicity prediction using deep learning. Frontiers in Environmental Science.3(80). DOI: 10.1016/j.aquatox.2004.11.002.
[4] T.-S. Chon, Y.-S. Park. (2006). Ecological informatics as an advanced interdisciplinary interpretation of ecosystems. Ecological Informatics.1:213-217. DOI: 10.1016/j.aquatox.2004.11.002.
[5] A. I. Dell, J. A. Bender, K. Branson, I. D. Couzin. et al.(2014). Automated image-based tracking and its application in ecology. Trends in Ecology & Evolution.29(7):417-428. DOI: 10.1016/j.aquatox.2004.11.002.
[6] A. Pérez-Escudero, J. Vicente-Page, R. C. Hinz, S. Arganda. et al.(2014). IdTracker: Tracking individuals in a group by automatic identification of unmarked animals. Nature Methods.11(7):743-748. DOI: 10.1016/j.aquatox.2004.11.002.
[7] T.-S. Chon. (2011). Self-organizing maps applied to ecological sciences. Ecological Informatics.6(1):50-61. DOI: 10.1016/j.aquatox.2004.11.002.
[8] C. W. Ji, S. H. Lee, K.-H. Choi, I.-S. Kwak. et al.(2007). Monitoring of movement behaviors of chironomid larvae after exposure to diazinon using fractal dimension and self-organizing map. International Journal of Ecodynamics.2(1):27-38. DOI: 10.1016/j.aquatox.2004.11.002.
[9] J. Spitzen, C. W. Spoor, F. Grieco, C. ter Braak. et al.(2013). A 3D Analysis of Flight Behavior of Anopheles gambiae sensu stricto Malaria Mosquitoes in Response to Human Odor and Heat. PLoS ONE.8(5). DOI: 10.1016/j.aquatox.2004.11.002.
[10] S. Lek, M. Scardi, P. F. M. Verdonschot, J.-P. Descy. et al.(2005). Modelling community structure in freshwater ecosystems. Modelling Community Structure in Freshwater Ecosystems:1-518. DOI: 10.1016/j.aquatox.2004.11.002.
[11] C. W. Ji, S. H. Lee, I. S. Kwak, E. Y. Cha. et al.(2006). Computational analysis of movement behaviors of medaka after the treatments of copper by using fractal dimension and artificial neural networks. Environmental Toxicology:93-107. DOI: 10.1016/j.aquatox.2004.11.002.
[12] K. Branson, A. A. Robie, J. Bender, P. Perona. et al.(2009). High-throughput ethomics in large groups of Drosophila. Nature Methods.6(6):451-457. DOI: 10.1016/j.aquatox.2004.11.002.
[13] Y. Li, J. M. Lee, T. S. Chon, Y. Liu. et al.(2013). Analysis of movement behavior of zebrafish (Daniorerio) under chemical stress using hidden Markov model. Modern Physics Letters B.27(02). DOI: 10.1016/j.aquatox.2004.11.002.
[14] J. Delcourt, M. Denoël, M. Ylieff, P. Poncin. et al.(2013). Video multitracking of fish behaviour: A synthesis and future perspectives. Fish and Fisheries.14(2):186-204. DOI: 10.1016/j.aquatox.2004.11.002.
[15] E. F. Morais, M. F. M. Campos, F. L. Padua, R. L. Carceroni. et al.Particle filter-based predictive tracking for robust fish counting. :367-374. DOI: 10.1016/j.aquatox.2004.11.002.
[16] Y. LeCun, Y. Bengio, G. Hinton. (2015). Deep learning. Nature.521(7553):436-444. DOI: 10.1016/j.aquatox.2004.11.002.
[17] M.-J. Bae, Y.-S. Park. (2014). Biological early warning system based on the responses of aquatic organisms to disturbances: A review. Science of the Total Environment.466-467:635-649. DOI: 10.1016/j.aquatox.2004.11.002.
[18] S. Kato, T. Nakagawa, M. Ohkawa, K. Muramoto. et al.(2004). A computer image processing system for quantification of zebrafish behavior. Journal of Neuroscience Methods.134(1):1-7. DOI: 10.1016/j.aquatox.2004.11.002.
[19] J. Ni, C. Zhang, L. Ren, S. X. Yang. et al.(2012). Abrupt event monitoring for water environment system based on KPCA and SVM. IEEE Transactions on Instrumentation and Measurement.61(4):980-989. DOI: 10.1016/j.aquatox.2004.11.002.
[20] A. Gerhardt. (1999). Recent trends in online biomonitoring for water quality control. Biomonitoring of Polluted Water-Reviews on Actual Topics. Environmental Research Forum.9:95-118. DOI: 10.1016/j.aquatox.2004.11.002.
[21] C.-K. Kim, I.-S. Kwak, E.-Y. Cha, T.-S. Chon. et al.(2006). Implementation of wavelets and artificial neural networks to detection of toxic response behavior of chironomids (Chironomidae: Diptera) for water quality monitoring. Ecological Modelling.195(1):61-71. DOI: 10.1016/j.aquatox.2004.11.002.
[22] T. F. Cootes, C. J. Taylor, D. H. Cooper, J. Graham. et al.(1995). Active shape models—their training and application. Computer Vision and Image Understanding.61(1):38-59. DOI: 10.1016/j.aquatox.2004.11.002.
[23] P. Przymus, K. Rykaczewski, R. Wiśniewski. (2011). Application of wavelets and kernel methods to detection and extraction of behaviours of freshwater mussels. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface.7105:43-54. DOI: 10.1016/j.aquatox.2004.11.002.
[24] Y. Liu, S.-H. Lee, T.-S. Chon. (2011). Analysis of behavioral changes of zebrafish (Danio rerio) in response to formaldehyde using Self-organizing map and a hidden Markov model. Ecological Modelling.222(14):2191-2201. DOI: 10.1016/j.aquatox.2004.11.002.
[25] V. M. Papadakis, I. E. Papadakis, F. Lamprianidou, A. Glaropoulos. et al.(2012). A computer-vision system and methodology for the analysis of fish behavior. Aquacultural Engineering.46(1):53-59. DOI: 10.1016/j.aquatox.2004.11.002.
[26] C. Xia, T.-S. Chon, Y. Liu, J. Chi. et al.(2016). Posture tracking of multiple individual fish for behavioral monitoring with visual sensors. Ecological Informatics.36:190-198. DOI: 10.1016/j.aquatox.2004.11.002.
[27] C. Bandt, B. Pompe. (2002). Permutation entropy: a natural complexity measure for time series. Physical Review Letters.88(17):1-4. DOI: 10.1016/j.aquatox.2004.11.002.
[28] I.-S. Kwak, T.-S. Chon, H.-M. Kang, N.-I. Chung. et al.(2002). Pattern recognition of the movement tracks of medaka (Oryzias latipes) in response to sub-lethal treatments of an insecticide by using artificial neural networks. Environmental Pollution.120(3):671-681. DOI: 10.1016/j.aquatox.2004.11.002.
[29] C. Xia, Y. Li, J. M. Lee. (2014). A visual measurement of fish locomotion based on deformable models. Intelligent Robotics and Applications.8917:110-116. DOI: 10.1016/j.aquatox.2004.11.002.
[30] Y. Liu, H. Li, Y. Q. Chen. (2012). Automatic Tracking of a Large Number of Moving Targets in 3D. Computer Vision – ECCV 2012.7575:730-742. DOI: 10.1016/j.aquatox.2004.11.002.
[31] Z. M. Qian, X. E. Cheng, Y. Q. Chen. (2014). Automatically detect and track multiple fish swimming in shallow water with frequent occlusion. PloS one.9(9). DOI: 10.1016/j.aquatox.2004.11.002.
[32] Y. Liu, T.-S. Chon, S.-H. Lee. (2010). Hidden Markov model and self-organizing map applied to exploration of movement behaviors of (Cladocera: Daphniidae). Journal of the Korean Physical Society.56(31):1003-1010. DOI: 10.1016/j.aquatox.2004.11.002.
[33] S. Fukuda, I. J. Kang, J. Moroishi, A. Nakamura. et al.(2010). The application of entropy for detecting behavioral responses in Japanese medaka (Oryzias latipes) exposed to different toxicants. Environmental Toxicology.25(5):446-455. DOI: 10.1016/j.aquatox.2004.11.002.
[34] I. Kuklina, A. Kouba, P. Kozák. (2013). Real-time monitoring of water quality using fish and crayfish as bio-indicators: A review. Environmental Modeling & Assessment.185(6):5043-5053. DOI: 10.1016/j.aquatox.2004.11.002.
[35] Z. Zivkovic. Improved adaptive Gaussian mixture model for background subtraction. :28-31. DOI: 10.1016/j.aquatox.2004.11.002.
[36] S. Kato, K. Tamada, Y. Shimada, T. Chujo. et al.(1996). A quantification of goldfish behaviorby an image processing system. Behavioural Brain Research.80(1):51-55. DOI: 10.1016/j.aquatox.2004.11.002.
[37] Y.-S. Park, N.-I. Chung, K.-H. Choi, E. Y. Cha. et al.(2005). Computational characterization of behavioral response of medaka (Oryzias latipes) treated with diazinon. Aquatic Toxicology.71(3):215-228. DOI: 10.1016/j.aquatox.2004.11.002.
[38] N. Otsu. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics.9(1):62-66. DOI: 10.1016/j.aquatox.2004.11.002.
[39] S. Masud, I. J. Singh, R. N. Ram. (2005). Behavioural and hematological responses of Cyprinus carpio exposed to mercurial chloride. Journal of Environmental Biology.26(2):393-397. DOI: 10.1016/j.aquatox.2004.11.002.
[40] S.-B. Lee, Y. Choe, T.-S. Chon, H. Y. Kang. et al.(2015). Analysis of zebrafish (Danio rerio) behavior in response to bacterial infection using a self-organizing map. BMC Veterinary Research.11(1, article no. 269). DOI: 10.1016/j.aquatox.2004.11.002.
[41] K. Suzuki, T. Takagi, T. Hiraishi. (2003). Video analysis of fish schooling behavior in finite space using a mathematical model. Fisheries Research.60(1):3-10. DOI: 10.1016/j.aquatox.2004.11.002.
[42] X. Jiang, D. Mojon. (2003). Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Transactions on Pattern Analysis and Machine Intelligence.25(1):131-137. DOI: 10.1016/j.aquatox.2004.11.002.
[43] Z. Ren, Z. Wang. (2010). Differences in the behavior characteristics between Daphnia magna and Japanese madaka in an on-line biomonitoring system. Journal of Environmental Sciences.22(5):703-708. DOI: 10.1016/j.aquatox.2004.11.002.
[44] R. Gerlai, V. Lee, R. Blaser. (2006). Effects of acute and chronic ethanol exposure on the behavior of adult zebrafish (Danio rerio). Pharmacology Biochemistry & Behavior.85(4):752-761. DOI: 10.1016/j.aquatox.2004.11.002.
[45] Z. Ren, N. Su, M. Miao, R. Fu. et al.(2012). Improvement of biological early warning system based on medaka (Oryzias latipes) behavioral responses to physiochemical factors. Journal of Biobased Materials and Bioenergy.6(6):678-681. DOI: 10.1016/j.aquatox.2004.11.002.
[46] A. D. Straw, K. Branson, T. R. Neumann, M. H. Dickinson. et al.(2011). Multi-camera real-time three-dimensional tracking of multiple flying animals. Journal of the Royal Society Interface.8(56):395-409. DOI: 10.1016/j.aquatox.2004.11.002.
[47] T. S. Chon, N. Chung, I. S. Kwak, J. S. Kim. et al.(2005). Movement behaviour of medaka (Oryzias latipes) in response to sublethal treatments of diazinon and cholinesterase activity in semi-natural conditions. Environmental Monitoring and Assessment.101(1–3):1-21. DOI: 10.1016/j.aquatox.2004.11.002.
[48] Y. Liu, T.-S. Chon, H. Baek, Y. Do. et al.(2011). Permutation entropy applied to movement behaviors of. Modern Physics Letters B.25(12-13):1133-1142. DOI: 10.1016/j.aquatox.2004.11.002.
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