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Methods and Algorithms for Intelligent Video Analytics in the Context of Solving Problems of Precision Pig Farming

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Supercomputing (RuSCDays 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14389))

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Abstract

The paper proposes an approach to developing a video data pipeline that addresses the basic tasks of precision pig farming. The pipeline performs both low-level tasks, such as data pre-processing, object detection, instance segmentation, tracking, object density estimation, etc., and high-level tasks, e.g. livestock counting, feeders and drinkers condition assessment, behavioral patterns analysis, estimation of livestock activity and weight, etc. The proposed solution is based on neural network algorithms and can be flexibly adjusted to specific conditions and tasks, including while sending emergency notifications. Furthermore, the system is architecturally capable of integrating additional sensor and input data. The approach is demonstrated by solving several problems in the fattening phase. The system has proven to have a number of competitive advantages, including stable operation in a high animal density environment.

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Authors and Affiliations

  1. Institute of Control Sciences, Russian Academy of Sciences, ul. Profsoyuznaya 65, Moscow, 117977, Russia

    Vsevolod Galkin & Andrey Makarenko

Authors
  1. Vsevolod Galkin
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  2. Andrey Makarenko
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Correspondence to Vsevolod Galkin .

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Editors and Affiliations

  1. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Vladimir Voevodin

  2. Research Computing Center (RCC), Moscow State University, Moscow, Russia

    Sergey Sobolev

  3. Russian Academy of Sciences (RAS), Keldysh Institute of Applied Mathematics, Moscow, Russia

    Mikhail Yakobovskiy

  4. Russian Federal Nuclear Center, Sarov, Russia

    Rashit Shagaliev

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Galkin, V., Makarenko, A. (2023). Methods and Algorithms for Intelligent Video Analytics in the Context of Solving Problems of Precision Pig Farming. In: Voevodin, V., Sobolev, S., Yakobovskiy, M., Shagaliev, R. (eds) Supercomputing. RuSCDays 2023. Lecture Notes in Computer Science, vol 14389. Springer, Cham. https://doi.org/10.1007/978-3-031-49435-2_16

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  • DOI: https://doi.org/10.1007/978-3-031-49435-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-49434-5

  • Online ISBN: 978-3-031-49435-2

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