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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
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    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. Juan Han & Jiaqi Wang, 2023. "Dairy Cow Nutrition and Milk Quality," Agriculture, MDPI, vol. 13(3), pages 1-2, March.
    3. 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.
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