Skip to main content
SpringerLink
Log in

A Pig Pose Estimation Model for Measuring Pig’s Body Size

  • Conference paper
  • First Online:
Advances in Computer Graphics (CGI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13443))

Included in the following conference series:

  • 1164 Accesses

Abstract

In this paper, we present a pig pose estimation model to solve the non-contact measurement of body size. The model includes the network header, down-sampling module, and up-sampling module. The network header includes the integration of image and image edge information. The original edge information in the image can be effectively used in the network, and the Canny operator calculates the edge information. The down-sampling module comprises residual structure and Triplet attention mechanism, which can effectively preserve the network context information while extracting image features. In the up-sampling module, the deconvolution method obtains the heat map containing the key point information. We also constructed a pig key point dataset to map pig key points with body size information. We can achieve 93.4% average precision by verifying our pig key point dataset. Compared with the 84.2% average precision of the baseline model, we achieved a 9.2% improvement.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Liu, T., Teng, G., Fu, W., Li, Z.: Extraction algorithms and applications of pig body size measurement points based on computer vision. Trans. Chin. Soc. Agric. Eng. 29, 161–168 (2013)

    Google Scholar 

  2. Wang, K., Guo, H., Liu, W., Ma, Q., Su, W., Zhu, D.: Extraction method of pig body size measurement points based on rotation normalization of point cloud. Trans. Chin. Soc. Agric. Eng. 33, 253–259 (2017)

    Google Scholar 

  3. Toshev, A., Szegedy, C.: Deeppose: human pose estimation via deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1653–1660 (2014)

    Google Scholar 

  4. Misra, D., Nalamada, T., Arasanipalai, A.U., Hou, Q.: Rotate to attend: convolutional triplet attention module. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 3139–3148 (2021)

    Google Scholar 

  5. Mu, J., Qiu, W., Hager, G.D., Yuille, A.L.: Learning from synthetic animals. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 12386–12395 (2020)

    Google Scholar 

  6. Yang, J., Xu, J., Lu, W., Zeng, D.: Computer vision-based body size measurement and weight estimation of large yellow croaker. J. Chin. Agric. Mech. 39, 70–74 (2018)

    Article  Google Scholar 

  7. Guo, H., Zhang, S., Ma, Q., Wang, P., Su, W., Zhu, D., Qi, B.: Cow body measurement based on Xtion. Trans. Chin. Soc. Agric. Eng. 30, 116–122 (2014)

    Google Scholar 

  8. Yongsheng, S., Lulu, A., Gang, L., Baocheng, L.: Ideal posture detection and body size measurement of pig based on Kinect. Nongye Jixie Xuebao/Trans. Chin. Soc. Agric. Mach. 50 (2019)

    Google Scholar 

  9. Zhang, A.L.N., Wu, B.P., Jiang, C.X.H., Xuan, D.C.Z., Ma, E.Y.H., Zhang, F.Y.A.: Development and validation of a visual image analysis for monitoring the body size of sheep. J. Appl. Anim. Res. 46, 1004–1015 (2018)

    Article  Google Scholar 

  10. Newell, A., Yang, K., Deng, J.: Stacked hourglass networks for human pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016. ECCV. Lecture Notes in Computer Science, vol. 9912, pp. 483–499. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_29

  11. Wei, S.-E., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4724–4732 (2016)

    Google Scholar 

  12. Xiao, B., Wu, H., Wei, Y.: Simple baselines for human pose estimation and tracking. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol. 11210, pp. 466–481. Springer, Cham(2018). https://doi.org/10.1007/978-3-030-01231-1_29

  13. Sun, K., Xiao, B., Liu, D., Wang, J.: Deep high-resolution representation learning for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5693–5703 (2019)

    Google Scholar 

  14. Psota, E.T., Mittek, M., Pérez, L.C., Schmidt, T., Mote, B.: Multi-pig part detection and association with a fully-convolutional network. Sensors 19, 852 (2019)

    Article  Google Scholar 

  15. Li, K., Wang, S., Zhang, X., Xu, Y., Xu, W., Tu, Z.: Pose recognition with cascade transformers. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1944–1953 (2021)

    Google Scholar 

  16. Liu, Z., et al.: Deep dual consecutive network for human pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 525–534 (2021)

    Google Scholar 

  17. Lee, S.K.: Pig pose estimation based on extracted data of mask R-CNN with VGG neural network for classifications. South Dakota State University (2020)

    Google Scholar 

  18. Li, C., Lee, G.H.: From synthetic to real: unsupervised domain adaptation for animal pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1482–1491 (2021)

    Google Scholar 

  19. Hõrak, H., Jermakovs, K., Haamer, R.E.: Modeling physical activity in children by combining raw hip-worn accelerometry, 2D pose estimation, and direct observation. IEEE Access 10, 39986–40000 (2022)

    Article  Google Scholar 

  20. Zhang, S., Jin, D., Dai, Y., Yang, F.: Relative Pose Estimation for Light Field Cameras Based on LF-Point-LF-Point Correspondence Model. IEEE Transactions on Image Processing 31, 1641–1656 (2022)

    Article  Google Scholar 

  21. Ocegueda-Hernández, V., Román-Godínez, I., Mendizabal-Ruiz, G.: A lightweight convolutional neural network for pose estimation of a planar model. Mach. Vis. Appl. 33, 1–21 (2022)

    Article  Google Scholar 

  22. Wang, F., et al..: Residual attention network for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3156–3164 (2017)

    Google Scholar 

  23. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  24. Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: Cbam: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds) Computer Vision – ECCV 2018. ECCV 2018. Lecture Notes in Computer Science, vol. 11211, pp. 3–19. Springer, Cham. https://doi.org/10.1007/978-3-030-01234-2_1

  25. Park, J., Woo, S., Lee, J.-Y., Kweon, I.S.: Bam: bottleneck attention module. arXiv preprint arXiv:1807.06514 (2018)

  26. Li, X., Wang, W., Hu, X., Yang, J.: Selective kernel networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 510–519 (2019)

    Google Scholar 

  27. Chen, Y., Kalantidis, Y., Li, J., Yan, S., Feng, J.: A^ 2-nets: double attention networks. Advances in Neural Information Processing Systems, vol. 31 (2018)

    Google Scholar 

  28. Gao, Z., Xie, J., Wang, Q., Li, P.: Global second-order pooling convolutional networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3024–3033 (2019)

    Google Scholar 

  29. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778. (2016)

    Google Scholar 

  30. Bochkovskiy, A., Wang, C.-Y., Liao, H.-Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)

Download references

Acknowledgements

This work was supported by Jiangsu Modern Agricultural Industry Key Technology Innovation Project under Grant CX(20)2013, the Key R&D Program of Jiangsu Province under Grant BE2019311 and National Key Research and Development Program under Grant 2020YFB160070301.

Author information

Authors and Affiliations

  1. School of Instrument Science and Engineering, Southeast University, Nanjing, 210096, China

    Yukun Yang, Wenhu Qin, Libo Sun & Weipeng Shi

Authors
  1. Yukun Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenhu Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Libo Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Weipeng Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Wenhu Qin or Libo Sun .

Editor information

Editors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Nadia Magnenat-Thalmann

  2. Bournemouth University, Poole, UK

    Jian Zhang

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. University of Crete, Heraklion, Greece

    George Papagiannakis

  5. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  6. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

  7. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yang, Y., Qin, W., Sun, L., Shi, W. (2022). A Pig Pose Estimation Model for Measuring Pig’s Body Size. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2022. Lecture Notes in Computer Science, vol 13443. Springer, Cham. https://doi.org/10.1007/978-3-031-23473-6_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-23473-6_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23472-9

  • Online ISBN: 978-3-031-23473-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation