Traulsen, Imke

[["dc.bibliographiccitation.firstpage","581"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","AgriEngineering"],["dc.bibliographiccitation.lastpage","595"],["dc.bibliographiccitation.volume","2"],["dc.contributor.author","Wutke, Martin"],["dc.contributor.author","Traulsen, Imke"],["dc.contributor.author","Gültas, Mehmet"],["dc.contributor.author","Schmitt, Armin Otto"],["dc.date.accessioned","2021-05-17T12:19:04Z"],["dc.date.accessioned","2021-10-27T13:20:03Z"],["dc.date.available","2021-05-17T12:19:04Z"],["dc.date.available","2021-10-27T13:20:03Z"],["dc.date.issued","2020"],["dc.description.abstract","The activity level of pigs is an important stress indicator which can be associated to tail-biting, a major issue for animal welfare of domestic pigs in conventional housing systems. Although the consideration of the animal activity could be essential to detect tail-biting before an outbreak occurs, it is often manually assessed and therefore labor intense, cost intensive and impracticable on a commercial scale. Recent advances of semi- and unsupervised convolutional neural networks (CNNs) have made them to the state of art technology for detecting anomalous behavior patterns in a variety of complex scene environments. In this study we apply such a CNN for anomaly detection to identify varying levels of activity in a multi-pen problem setup. By applying a two-stage approach we first trained the CNN to detect anomalies in the form of extreme activity behavior. Second, we trained a classifier to categorize the detected anomaly scores by learning the potential activity range of each pen. We evaluated our framework by analyzing 82 manually rated videos and achieved a success rate of 91%. Furthermore, we compared our model with a motion history image (MHI) approach and a binary image approach using two benchmark data sets, i.e., the well established pedestrian data sets published by the University of California, San Diego (UCSD) and our pig data set. The results show the effectiveness of our framework, which can be applied without the need of a labor intense manual annotation process and can be utilized for the assessment of the pig activity in a variety of applications like early warning systems to detect changes in the state of health."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2020"],["dc.identifier.doi","10.3390/agriengineering2040039"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17775"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/91934"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","2624-7402"],["dc.relation.orgunit","Fakultät für Agrarwissenschaften"],["dc.rights","CC BY 4.0"],["dc.rights.access","openAccess"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","630"],["dc.title","Investigation of Pig Activity Based on Video Data and Semi-Supervised Neural Networks"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","170"],["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Sensors"],["dc.bibliographiccitation.lastpage","13"],["dc.bibliographiccitation.volume","18"],["dc.contributor.author","Scheel, Christoph"],["dc.contributor.author","Auer, Wolfgang"],["dc.contributor.author","Burfeind, Onno"],["dc.contributor.author","Krieter, Joachim"],["dc.contributor.author","Traulsen, Imke"],["dc.date.accessioned","2019-07-09T11:45:17Z"],["dc.date.available","2019-07-09T11:45:17Z"],["dc.date.issued","2018"],["dc.description.abstract","The aim of the present study was to automatically predict the onset of farrowing in crate-confined sows. (1) Background: Automatic tools are appropriate to support animal surveillance under practical farming conditions. (2) Methods: In three batches, sows in one farrowing compartment of the Futterkamp research farm were equipped with an ear sensor to sample acceleration. As a reference video, recordings of the sows were used. A classical CUSUM chart using different acceleration indices of various distribution characteristics with several scenarios were compared. (3) Results: The increase of activity mainly due to nest building behavior before the onset of farrowing could be detected with the sow individual CUSUM chart. The best performance required a statistical distribution characteristic that represented fluctuations in the signal (for example, 1st variation) combined with a transformation of this parameter by cumulating differences in the signal within certain time periods from one day to another. With this transformed signal, farrowing sows could reliably be detected. For 100% or 85% of the sows, an alarm was given within 48 or 12 h before the onset of farrowing. (4) Conclusions: Acceleration measurements in the ear of a sow are suitable for detecting the onset of farrowing in individually housed sows in commercial farrowing crates."],["dc.identifier.doi","10.3390/s18010170"],["dc.identifier.pmid","29320395"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15098"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59202"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","MDPI"],["dc.relation.eissn","1424-8220"],["dc.relation.issn","1424-8220"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","630"],["dc.title","Using Acceleration Data to Automatically Detect the Onset of Farrowing in Sows."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]