Multi-pig Pose Estimation Using DeepLabCut

Fahimeh Farahnakian, Jukka Heikkonen, Stefan Bjorkman

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Pose estimation towards providing the assessments of animal health and welfare monitoring has strongly gained interest in the last few years. However, it is a challenging computer vision problem as the frequent interaction causes occlusions the association of detected key-points to the correct individuals. Deep Learning (DL) offers major advances in the field of pose estimation. In this paper, we investigated the possibility of using a famous open-source DL-based toolbox, DeepLabCut [1], for the specific pig pose estimation task. We predicted the body part of each individual pig from only input images or video sequences directly with no adaptations to the application setting. We used a real dataset which contains 2000 annotated images with 24,842 individually annotated pigs from 17 different locations and light conditions. The experimental results demonstrated that we can achieve a small root mean square error between the manual and predicted labels (10.1) when detecting pigs in environments previously seen by a DL model during training. To evaluate the robustness of the trained model, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 12.0 root mean square error.

Original languageEnglish
Title of host publication11th International Conference on Intelligent Control and Information Processing, ICICIP 2021
Number of pages6
Publication date2021
ISBN (Print)978-1-6654-2516-2
ISBN (Electronic)978-1-6654-2515-5
Publication statusPublished - 2021
MoE publication typeA4 Article in conference proceedings
EventInternational Conference on Intelligent Control and Information Processing - Dali, China
Duration: 3 Dec 20217 Dec 2021
Conference number: 11

Fields of Science

  • Animal pose estimation
  • convolutional neural network
  • deep learning
  • 113 Computer and information sciences
  • 413 Veterinary science

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