{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T01:42:02Z","timestamp":1781055722705,"version":"3.54.1"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2016,5,2]],"date-time":"2016-05-02T00:00:00Z","timestamp":1462147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s16050631","type":"journal-article","created":{"date-parts":[[2016,5,2]],"date-time":"2016-05-02T10:17:11Z","timestamp":1462184231000},"page":"631","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":139,"title":["Automatic Recognition of Aggressive Behavior in Pigs Using a Kinect Depth Sensor"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2077-4850","authenticated-orcid":false,"given":"Jonguk","family":"Lee","sequence":"first","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Jin","sequence":"additional","affiliation":[{"name":"Ctrip Co., 99 Fu Quan Road, IT Security Center, Shanghai 200335, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daihee","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongwha","family":"Chung","sequence":"additional","affiliation":[{"name":"Department of Computer and Information Science, Korea University, Sejong Campus, Sejong City 30019, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,5,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"12929","DOI":"10.3390\/s131012929","article-title":"Automatic detection and recognition of pig wasting diseases using sound data in audio surveillance systems","volume":"13","author":"Chung","year":"2013","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1030","DOI":"10.5713\/ajas.2012.12628","article-title":"Automatic detection of cow\u2019s oestrus in audio surveillance system","volume":"26","author":"Chung","year":"2013","journal-title":"Asian Austr. 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