access icon free Tracking of group-housed pigs using multi-ellipsoid expectation maximisation

Maintaining the health and well-being of animals is critical to the efficiency and profitability of livestock operations. However, it can be difficult to monitor the health of animals in large group-housed settings without the assistance of technology. This study presents a system that uses depth images to continuously track individual pigs in a group-housed environment. It is an alternative to traditional manual observation used by both researchers and producers for the analysis of animal activities and behaviours. The tracking method used by the system exploits the consistent shape and fixed number of the targets in the environment by applying expectation maximisation as a policy for fitting an ellipsoid to each target. Results demonstrate that the system can maintain the correct positions and orientations of 15 group-housed pigs for an average of 19.7 min between failure events.

Inspec keywords: expectation-maximisation algorithm; tracking; computer vision; farming

Other keywords: tracking method; group-housed pigs tracking; group-housed environment; failure events; multiellipsoid expectation maximisation; animal behaviours; depth images; animal activities

Subjects: Computer vision and image processing techniques; Optical, image and video signal processing; Other topics in statistics; Other topics in statistics; Agriculture, forestry and fisheries computing

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