Moo-ving Beyond Tradition: Revolutionizing Cattle Behavioural Phenotyping with Pose Estimation Techniques
Navid Ghassemi, Ali Goldani, Ian Q. Whishaw, Majid H. Mohajerani

TL;DR
This paper reviews recent advancements in pose estimation techniques for cattle, highlighting their potential to improve health monitoring, behavioral analysis, and welfare in the cattle industry, and proposes a collaborative open science platform.
Contribution
It provides a comprehensive review of pose estimation methods for cattle and introduces a platform to foster industry-academia collaboration.
Findings
Pose estimation enables precise cattle movement analysis.
Current methods face challenges in scalability and accuracy.
A new open science platform is proposed to advance research.
Abstract
The cattle industry has been a major contributor to the economy of many countries, including the US and Canada. The integration of Artificial Intelligence (AI) has revolutionized this sector, mirroring its transformative impact across all industries by enabling scalable and automated monitoring and intervention practices. AI has also introduced tools and methods that automate many tasks previously performed by human labor with the help of computer vision, including health inspections. Among these methods, pose estimation has a special place; pose estimation is the process of finding the position of joints in an image of animals. Analyzing the pose of animal subjects enables precise identification and tracking of the animal's movement and the movements of its body parts. By summarizing the video and imagery data into movement and joint location using pose estimation and then analyzing…
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Taxonomy
TopicsAnimal Behavior and Welfare Studies
