Tracking of group-housed pigs using multiellipsoid expectation maximisation

Mateusz Mittek, Eric T. Psota, Jay D. Carlson, Lance C. Pérez, Ty Schmidt, Benny Mote

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)121-128
Number of pages8
JournalIET Computer Vision
Volume12
Issue number2
DOIs
StatePublished - Mar 1 2018

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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