IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v13y2023i8p1535-d1208284.html
   My bibliography  Save this article

Study of Pose Estimation Based on Spatio-Temporal Characteristics of Cow Skeleton

Author

Listed:
  • Yongfeng Wei

    (School of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China
    These authors contributed equally to this work.)

  • Hanmeng Zhang

    (School of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China
    These authors contributed equally to this work.)

  • Caili Gong

    (School of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China)

  • Dong Wang

    (School of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China)

  • Ming Ye

    (School of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China)

  • Yupu Jia

    (School of Electronic Information Engineering, Inner Mongolia University, Hohhot 010021, China)

Abstract

The pose of cows reflects their body condition, and the information contained in the skeleton can provide data support for lameness, estrus, milk yield, and contraction behavior detection. This paper presents an algorithm for automatically detecting the condition of cows in a real farm environment based on skeleton spatio-temporal features. The cow skeleton is obtained by matching Partial Confidence Maps (PCMs) and Partial Affinity Fields (PAFs). The effectiveness of skeleton extraction was validated by testing 780 images for three different poses (standing, walking, and lying). The results indicate that the Average Precision of Keypoints (APK) for the pelvis is highest in the standing and lying poses, achieving 89.52% and 90.13%, respectively. For walking, the highest APK for the legs was 88.52%, while the back APK was the lowest across all poses. To estimate the pose, a Multi-Scale Temporal Convolutional Network (MS-TCN) was constructed, and comparative experiments were conducted to compare different attention mechanisms and activation functions. Among the tested models, the CMS-TCN with Coord Attention and Gaussian Error Linear Unit (GELU) activation functions achieved precision, recall, and F1 scores of 94.71%, 86.99%, and 90.69%, respectively. This method demonstrates a relatively high detection rate, making it a valuable reference for animal pose estimation in precision livestock farming.

Suggested Citation

  • Yongfeng Wei & Hanmeng Zhang & Caili Gong & Dong Wang & Ming Ye & Yupu Jia, 2023. "Study of Pose Estimation Based on Spatio-Temporal Characteristics of Cow Skeleton," Agriculture, MDPI, vol. 13(8), pages 1-14, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1535-:d:1208284
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/13/8/1535/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/13/8/1535/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. John McDonagh & Georgios Tzimiropoulos & Kimberley R. Slinger & Zoë J. Huggett & Peter M. Down & Matt J. Bell, 2021. "Detecting Dairy Cow Behavior Using Vision Technology," Agriculture, MDPI, vol. 11(7), pages 1-8, July.
    2. Marisanna Speroni & Massimo Malacarne & Federico Righi & Piero Franceschi & Andrea Summer, 2018. "Increasing of Posture Changes as Indicator of Imminent Calving in Dairy Cows," Agriculture, MDPI, vol. 8(11), pages 1-11, November.
    3. Juan Han & Jiaqi Wang, 2023. "Dairy Cow Nutrition and Milk Quality," Agriculture, MDPI, vol. 13(3), pages 1-2, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Andrea Colantoni & Nicola Lacetera & Loredana Basiricò & Massimo Malacarne & Andrea Summer & Umberto Bernabucci, 2022. "Innovative Technologies for the Feeding of Dairy Cattle to Ensure Animal Welfare and Production Quality—INNOVALAT," Agriculture, MDPI, vol. 12(5), pages 1-4, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1535-:d:1208284. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.