Infrared reflection system for indoor 3D tracking of fish

Pautsina, Aliaksandr; Císař, Petr; Štys, Dalibor; Terjesen, Bendik Fyhn; Espmark, Åsa Maria Olofsdotter

doi:10.1016/j.aquaeng.2015.09.002

Cited by 47 publications

(36 citation statements)

References 19 publications

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“…In fact, a near‐infrared system has already been deployed and has achieved very good results in the 3D tracking of fish movements (Pautsina et al . ). The operating principle is based on the strong absorption of light in the near‐infrared range by water, allowing the distance to a fish to be estimated based on the corresponding brightness of the fish in the image.…”

Section: Feeding Control Methods Based On Computer Visionmentioning

confidence: 97%

“…According to recent research by Shcherbakov et al (2013), an 850 nm wavelength nearinfrared light source will not affect the normal growth of fish and is consequently suitable for monitoring fish behaviour in aquaculture systems with poor lighting. In fact, a near-infrared system has already been deployed and has achieved very good results in the 3D tracking of fish movements (Pautsina et al 2015). The operating principle is based on the strong absorption of light in the near-infrared range by water, allowing the distance to a fish to be estimated based on the corresponding brightness of the fish in the image.…”

Section: Individual Feature Analysismentioning

confidence: 99%

See 1 more Smart Citation

Intelligent feeding control methods in aquaculture with an emphasis on fish: a review

Zhou

Kai

et al. 2017

Reviews in Aquaculture

141

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In aquaculture, feeding is the primary factor determining efficiency and cost, so it is important to know when to stop feeding to maximize efficiency. Until now, fish feeding has been mostly based on artificial discrimination, which is usually time‐consuming and laborious. In recent years, intelligent feeding control according to changes in behaviour and growth status has gained increasing attention. This approach involves many methods as well as monitoring and feedback equipment and can automatically determine the feeding demands of fish. This review summarizes the development of intelligent feeding control methods, such as mathematical models, acoustic methods and computer vision, in aquaculture over the past three decades. All methods have unique application scenarios and models for the culture to which they are most suitable, and the advantages and disadvantages of each method in the laboratory as well as in pond, cage and recirculating aquaculture systems are analysed. Studies show that improvements in sensor accuracy and hardware and software processing speed have promoted the development of new technologies and methods, providing effective or potential support for intelligent feeding control. However, its accuracy and intelligent are still need to be improved to meet the needs of actual feeding scenarios. Through close collaborations between engineers and fish behaviourists, the feeding machine and system will be more elaborate and precise on the basis of the above methods, and the level of intelligence will be further improved.

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Section: Feeding Control Methods Based On Computer Visionmentioning

confidence: 97%

Section: Individual Feature Analysismentioning

confidence: 99%

Intelligent feeding control methods in aquaculture with an emphasis on fish: a review

Zhou

Kai

et al. 2017

Reviews in Aquaculture

141

View full text Add to dashboard Cite

show abstract

“…Subsequent research by this author was also applied to this method. Pautsina et al used a new infrared reflection system for indoor 3D tracking to automatically monitor fish (Pautsina et al., 2015). This method estimates fish distance based on the absorption effect of water on the near‐infrared (NIR) range of light.…”

Section: Individual/group Target Detectionmentioning

confidence: 99%

Application of computer vision in fish intelligent feeding system—A review

Jiang

Wei

et al. 2020

Aquac. Res.

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“…The quantitative methods include two kinds. The first kind of method is based on appearance feature to recognize the objects and obtain the behavior trajectory, such as shape matching [ 5 ] and infrared reflection [ 6 ]. The second kind of method is based on image processing to recognize the objects and obtain the behavior trajectory [ 7 ], such as the inter-frame difference method [ 8 ] and optical flow method [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish

Zhu

et al. 2020

Sensors

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Ammonia can be produced by the respiration and excretion of fish during the farming process, which can affect the life of fish. In this paper, to research the behavior of fish under different ammonia concentration and make the corresponding judgment and early warning for the abnormal behavior of fish, the different ammonia environments are simulated by adding the ammonium chloride into the water. Different from the existing methods of directly artificial observation or artificial marking, this paper proposed a recognition and analysis of behavior trajectory approach based on deep learning. Firstly, the three-dimensional spatial trajectories of fish are drawn by three-dimensional reconstruction. Then, the influence of different concentrations of ammonia on fish is analyzed according to the behavior trajectory of fish in different concentrations of ammonia. The results of comparative experiments show that the movement of fish and vitality decrease significantly, and the fish often stagnates in the water of containing ammonium chloride. The proposed approach can provide a new idea for the behavior analysis of animal.

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Infrared reflection system for indoor 3D tracking of fish

Cited by 47 publications

References 19 publications

Intelligent feeding control methods in aquaculture with an emphasis on fish: a review

Intelligent feeding control methods in aquaculture with an emphasis on fish: a review

Application of computer vision in fish intelligent feeding system—A review

Analysis of Behavior Trajectory Based on Deep Learning in Ammonia Environment for Fish

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