Automated Monitoring of Ear Biting in Pigs by Tracking Individuals and Events

Anicetus Odo, Niall McLaughlin, Ilias Kyriazakis; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 7095-7103

We propose a system for automated monitoring of ear-biting in pigs. Ear-biting presents a welfare challenge to commercial pig farming, leading to injuries and infections that affect animal welfare. We use a computer vision system to detect and track all pigs and ear-biting events. Our goal is to provide early warning of ear-biting to allow quick intervention to improve the health and welfare of commercial farm animals. We compare several different object detection methods for the detection of individual pigs, including an oriented bounding box detector, which is better suited to the accurate detection of pigs from overhead cameras. We track all pigs and all ear-biting events using a specialised two-stage multi-object tracking system. The tracking system is adapted to match the characteristics of each entity being tracked. The tracking system allows the individual pigs involved in an ear-biting incident to be identified, allowing for targeted welfare interventions. We evaluate our complete system on real farm videos and demonstrate that our complete system improves compared to existing ear-biting detection methods.