Open AccessTracking of group‐housed pigs using multi‐ellipsoid expectation maximisation
Author(s) -
Mittek Mateusz,
Psota Eric T.,
Carlson Jay D.,
Pérez Lance C.,
Schmidt Ty,
Mote Benny
Publication year - 2018
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2017.0085
Subject(s) - profitability index , ellipsoid , tracking (education) , animal health , computer science , computer vision , exploit , artificial intelligence , group (periodic table) , tracking system , track (disk drive) , zoology , geography , biology , business , psychology , computer security , finance , pedagogy , geodesy , filter (signal processing) , operating system , chemistry , organic chemistry
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.