{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T06:26:57Z","timestamp":1773383217683,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2020,4,22]],"date-time":"2020-04-22T00:00:00Z","timestamp":1587513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012167","name":"National Science and Technology Infrastructure Program","doi-asserted-by":"publisher","award":["4444-10099609"],"award-info":[{"award-number":["4444-10099609"]}],"id":[{"id":"10.13039\/501100012167","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The statistical data of different kinds of behaviors of pigs can reflect their health status. However, the traditional behavior statistics of pigs were obtained and then recorded from the videos through human eyes. In order to reduce labor and time consumption, this paper proposed a pig behavior recognition network with a spatiotemporal convolutional network based on the SlowFast network architecture for behavior classification of five categories. Firstly, a pig behavior recognition video dataset (PBVD-5) was built by cutting short clips from 3-month non-stop shooting videos, which was composed of five categories of pig\u2019s behavior: feeding, lying, motoring, scratching and mounting. Subsequently, a SlowFast network based spatiotemporal convolutional network for the pig\u2019s multi-behavior recognition (PMB-SCN) was proposed. The results of the networks with variant architectures of the PMB-SCN were implemented and the optimal architecture was compared with the state-of-the-art single stream 3D convolutional network in our dataset. Our 3D pig behavior recognition network showed a top-1 accuracy of 97.63% and a views accuracy of 96.35% on the test set of PBVD and a top-1 accuracy of 91.87% and a views accuracy of 84.47% on a new test set collected from a completely different pigsty. The experimental results showed that this network provided remarkable ability of generalization and possibility for the subsequent pig detection and behavior recognition simultaneously.<\/jats:p>","DOI":"10.3390\/s20082381","type":"journal-article","created":{"date-parts":[[2020,4,23]],"date-time":"2020-04-23T10:46:22Z","timestamp":1587638782000},"page":"2381","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A Spatiotemporal Convolutional Network for Multi-Behavior Recognition of Pigs"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3656-1358","authenticated-orcid":false,"given":"Dan","family":"Li","sequence":"first","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6920-3787","authenticated-orcid":false,"given":"Kaifeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]},{"given":"Zhenbo","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]},{"given":"Yifei","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1017\/S1751731116001208","article-title":"A new approach for categorizing pig lying behavior based on a Delaunay triangulation method","volume":"11","author":"Nasirahmadi","year":"2017","journal-title":"Animal"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.biosystemseng.2015.10.002","article-title":"Monitoring pig movement at the slaughterhouse using optical flow and modified angular histograms","volume":"141","author":"Gronskyte","year":"2016","journal-title":"Biosyst. 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