Study on fish movement monitoring method based on image processing

YUAN Yongming; SHI Pei

doi:10.3969/j.issn.2095-0780.2018.05.014

Volume 14 Issue 5

Sep. 2018

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South China Fisheries Science > 2018 > 14(5): 109-114. > DOI: 10.3969/j.issn.2095-0780.2018.05.014

YUAN Yongming, SHI Pei. Study on fish movement monitoring method based on image processing[J]. South China Fisheries Science, 2018, 14(5): 109-114. DOI: 10.3969/j.issn.2095-0780.2018.05.014

Citation:

YUAN Yongming, SHI Pei. Study on fish movement monitoring method based on image processing[J]. South China Fisheries Science, 2018, 14(5): 109-114. DOI: 10.3969/j.issn.2095-0780.2018.05.014

Citation:

YUAN Yongming, SHI Pei. Study on fish movement monitoring method based on image processing[J]. South China Fisheries Science, 2018, 14(5): 109-114. DOI: 10.3969/j.issn.2095-0780.2018.05.014

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Study on fish movement monitoring method based on image processing

YUAN Yongming^{1, 2,},
SHI Pei¹

1.
National Special Freshwater Fishery Industry Research Center; Freshwater Fisheries Research Center, Chinese Academy of Fishery Sciences, Wuxi 214081, China
2.
Key Laboratory of Freshwater Fisheries and Germplasm Resources Utilization, Ministry of Agriculture and Rural Affairs, Wuxi 214081, China

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Received Date: April 07, 2018
Revised Date: May 01, 2018
Available Online: December 04, 2018

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Abstract

Abstract

Observation of fish behavior provides rich visual information for fish health monitoring. However, the method of monitoring the fish behavior by manual marking is time-consuming and inefficient. In order to solve the problem of fish behavior monitoring, a method of monitoring tilapia movement based on image processing is proposed. These fish movement videos were first collected by computer and CCD camera, and then pretreated by graying and filtering. The Canny detection algorithm improved by Otsu was used to extract the edge of fish. Based on modelling the motion of fish school and combining the objective matching algorithm, the tracking and monitoring of fish school can be realized well. The results show that the individual detection rate of fish school was 98.96%, and the trajectory available factor (TAF) was 97%. The proposed algorithm can improve monitoring performance, better than Kalman algorithm, and can realize fish school tracking and monitoring.
- fish school,
- movement behavior,
- monitoring,
- image processing,
- extended Kalman filter

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