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Volume: 34 | Article ID: IRIACV-276
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Deep learning-based multiple animal pose estimation
  DOI :  10.2352/EI.2022.34.6.IRIACV-276  Published OnlineJanuary 2022
Abstract
Abstract

We proposed a deep learning-based approach for pig keypoint detection. In a nutshell, we explored transfer learning to adapt a human pose estimation model for the pigs. In total, we tested three different models and eventually trained openpose on the pig data. For training, the data is annotated in COCO format. Additionally, we visualized the pixel level response of the network named PAF (part infinity field) on the test frames to highlight the model learning capabilities. The trained model shows promising results and open new a door for further research.

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Brage Arnkærn, Sigurd Schoeler, Mohib Ullah, Faouzi Alaya Cheikh, "Deep learning-based multiple animal pose estimationin Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2022,  pp 276-1 - 276-6,  https://doi.org/10.2352/EI.2022.34.6.IRIACV-276

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