Citation

Lee J, Jin L, Park D, Chung Y. Sensors (Basel) 2016; 16(5): s16050631.

Affiliation

Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea. ychungy@korea.ac.kr.

Copyright

(Copyright © 2016, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s16050631

PMID

27144572

Abstract

Aggression among pigs adversely affects economic returns and animal welfare in intensive pigsties. In this study, we developed a non-invasive, inexpensive, automatic monitoring prototype system that uses a Kinect depth sensor to recognize aggressive behavior in a commercial pigpen. The method begins by extracting activity features from the Kinect depth information obtained in a pigsty. The detection and classification module, which employs two binary-classifier support vector machines in a hierarchical manner, detects aggressive activity, and classifies it into aggressive sub-types such as head-to-head (or body) knocking and chasing. Our experimental results showed that this method is effective for detecting aggressive pig behaviors in terms of both cost-effectiveness (using a low-cost Kinect depth sensor) and accuracy (detection and classification accuracies over 95.7% and 90.2%, respectively), either as a standalone solution or to complement existing methods.

Language: en