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Fish Behavior Analysis Based on Computer Vision: A Survey

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Data Science (ICPCSEE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1059))

Abstract

Fish behavior refers to various movements of fish. Fish behavior is closely related to the ecology of fish, physiological changes of fish, aquaculture and so on. Related applications will be expanded if fish behavior is analyzed properly. Traditional analysis of fish behavior mainly relies on the observation of human eyes. With the deepening and extension of application and the rapid development of computer technology, computer vision technology is increasingly used to analyze fish behaviors. This paper summarized the research status, research progress and main problems of fish behavior analysis by using computer vision and made forecast about future research.

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References

  1. Henriques, S., Cardoso, P.: Processes underpinning fish species composition patterns in estuarine ecosystems worldwide. J. Biogeogr. 44(3), 627–639 (2016)

    Article  Google Scholar 

  2. Popper, A.N., Carlson, T.J.: Application of sound and other stimuli to control fish behavior. Trans. Am. Fish. Soc. 127(5), 673–707 (1998)

    Article  Google Scholar 

  3. Johansson, G.: Visual motion perception. Sci. Am. 232(6), 76–89 (1975)

    Article  Google Scholar 

  4. Favorskaya, M.N., Jain, L.C.: Development of mathematical theory in computer vision. In: Favorskaya, M.N., Jain, L.C. (eds.) Computer Vision in Control Systems-1. ISRL, vol. 73, pp. 1–8. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-10653-3_1

    Chapter  Google Scholar 

  5. Lu, H.D., Liu, Y.: Design and implementation of fish behavior automatic monitoring system based on computer vision. Fish. Mod. 21(21), 39–50 (2011)

    Google Scholar 

  6. Kang, I.J., Moroishi, J.: Biological monitoring for detection of toxic chemicals in water by the swimming behavior of small freshwater fish. J.- Fac. Agric. Kyushu Univ. 54(1), 209–214 (2009)

    Google Scholar 

  7. Israeli, D., Kimmel, E.: Monitoring the behavior of hypoxia-stressed Carassius auratus using computer vision. Aquacult. Eng. 15(6), 423–440 (1996)

    Article  Google Scholar 

  8. Fan, L., Liu, Y.: Fish motion detecting algorithms based on computer vision technologies. Trans. Chin. Soc. Agric. Eng. 27(7), 226–230 (2011)

    Google Scholar 

  9. Nguyen, N.D., Huynh, K.N.: Fish detection and movement tracking. In: 2015 International Conference on Advanced Technologies for Communications, pp. 484–489. IEEE (2015)

    Google Scholar 

  10. Zhao, X., Peng, H.: A video image detection method for fish motion characteristics. J. Xi’an Univ. Posts Telecommun. (2), 7 (2017)

    Google Scholar 

  11. Rodríguez, Á.: Fish tracking in vertical slot fishways using computer vision techniques. https://doi.org/10.2166/hydro.2014.034/. Accessed 10 Oct 2018

    Article  Google Scholar 

  12. Rodriguez, A., Rabuñal, J.R., Bermudez, M., Puertas, J.: Detection of fishes in turbulent waters based on image analysis. In: Ferrández Vicente, J.M., Álvarez Sánchez, J.R., de la Paz López, F., Toledo Moreo, F.J. (eds.) IWINAC 2013. LNCS, vol. 7931, pp. 404–412. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38622-0_42

    Chapter  Google Scholar 

  13. Wang, N.X.R.: Automated analysis of wild fish behavior in a natural habitat. In: Proceedings of the 2nd International Workshop on Environmental Multimedia Retrieval, pp. 21–26. ACM (2015)

    Google Scholar 

  14. Xiao, G., Fan, W.K.: Research of the fish tracking method with occlusion based on monocular stereo visions. In: 2016 International Conference on Information System and Artificial Intelligence, (ISAI), pp. 581–589 (2017)

    Google Scholar 

  15. Ren, Z., Chon, T.S.: The monitoring and assessment of aquatic toxicology. Biomed. Res. Int. 2017, 1–2 (2017)

    Google Scholar 

  16. Li, X., Fan, L.: Preference habit of juvenile turbot for different color backgrounds based on computer vision. Trans. Chin. Soc. Agric. Eng. 28(10), 189–193 (2012)

    Google Scholar 

  17. Papadakis, V.M.: A computer-vision system and methodology for the analysis of fish behavior. Aquacult. Eng. 46, 53–59 (2017)

    Article  Google Scholar 

  18. Zhao, J., Gu, Z.: Spatial behavioral characteristics and statistics-based kinetic energy modeling in special behaviors detection of a shoal of fish in a recirculating aquaculture system. Comput. Electron. Agric. 127, 271–280 (2016)

    Article  Google Scholar 

  19. Hu, J., Li, D.: Fish species classification by color, texture and multi-class support vector machine using computer vision. Comput. Electron. Agric. 88, 133–140 (2012)

    Article  Google Scholar 

  20. Tang, Y.P., Liu, S.S.: A study of fish behavior analysis based on 3D computer vision”. High Technol. Commun. 25(3), 249–256 (2015)

    Google Scholar 

  21. Liu, S.S.: Research on the key technology of the seismic behavior analysis of fish based on 3D computer vision. Zhejiang University of Technology (2016)

    Google Scholar 

  22. Yu, X., Hou, X.: Anomaly detection of fish school behavior based on features statistical and optical flow methods. Trans. Chin. Soc. Agric. Eng. 30(2), 162–168 (2014)

    Google Scholar 

  23. Xu, S., Xing, K.Z.: A method for identifying abnormal behavior of point grouper based on machine vision. Fish. Mod. 43(1), 18–23 (2016)

    Google Scholar 

  24. Cerdà, V., Estela, J.M.: Flow techniques in water analysis. Talanta 50(4), 695–705 (1999)

    Article  Google Scholar 

  25. Zhou, C., Zhang, B.: Near-infrared imaging to quantify the feeding behavior of fish in aquaculture. Comput. Electron. Agric. 135, 233–241 (2017)

    Article  Google Scholar 

  26. Liu, Z., Li, X.: Measuring feeding activity of fish in RAS using computer vision. Aquacult. Eng. 60(3), 20–27 (2014)

    Article  Google Scholar 

  27. Ye, Z., Zhao, J.: Behavioral characteristics and statistics-based imaging techniques in the assessment and optimization of tilapia feeding in a recirculating aquaculture system. Trans. ASABE 59(1), 345–355 (2016)

    Article  Google Scholar 

  28. Deng, L., Yu, D.: Deep learning: methods and applications. Found. Trends® Sig. Process. 7(3–4), 197–387 (2014)

    Article  MathSciNet  Google Scholar 

  29. Neshat, M., Sepidnam, G.: Artificial fish swarm algorithm: a survey of the state-of-the-art, hybridization, combinatorial and indicative applications. Artif. Intell. Rev. 42(4), 965–997 (2014)

    Article  Google Scholar 

  30. Sevilla-Lara, L., Liao, Y., Güney, F., Jampani, V., Geiger, A., Black, Michael J.: On the integration of optical flow and action recognition. In: Brox, T., Bruhn, A., Fritz, M. (eds.) GCPR 2018. LNCS, vol. 11269, pp. 281–297. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12939-2_20

    Chapter  Google Scholar 

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Acknowledgements

This work is supported by Guangdong Province Key Laboratory of Popular High Performance Computers (SZU-GDPHPCL201805), Institute of Marine Industry Technology of Universities in Liaoning Province (2018-CY-34), National Natural Science Foundation of China (61701070), Liaoning Doctoral Start-up Fund (20180540090) and China Postdoctoral Science Foundation (2018M640239).

Author information

Authors and Affiliations

  1. School of Information Engineering, Dalian Ocean University, Dalian, 116023, China

    Yizhi Zhou, Hong Yu, Junfeng Wu, Zhen Cui & Fangyan Zhang

  2. Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen University, Shenzhen, 518060, China

    Yizhi Zhou, Hong Yu, Junfeng Wu, Zhen Cui & Fangyan Zhang

  3. School of Computer Science and Technology, Tianjin University, Tianjin, 300072, China

    Junfeng Wu

Authors
  1. Yizhi Zhou
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  2. Hong Yu
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  3. Junfeng Wu
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  4. Zhen Cui
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  5. Fangyan Zhang
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Corresponding author

Correspondence to Junfeng Wu .

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Editors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Rui Mao

  2. Harbin Institute of Technology, Harbin, China

    Hongzhi Wang

  3. Guilin University of Technology, Guilin, China

    Xiaolan Xie

  4. National Academy of Guo Ding Institute of Data Science, Harbin, China

    Zeguang Lu

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Zhou, Y., Yu, H., Wu, J., Cui, Z., Zhang, F. (2019). Fish Behavior Analysis Based on Computer Vision: A Survey. In: Mao, R., Wang, H., Xie, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1059. Springer, Singapore. https://doi.org/10.1007/978-981-15-0121-0_10

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  • DOI: https://doi.org/10.1007/978-981-15-0121-0_10

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0120-3

  • Online ISBN: 978-981-15-0121-0

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