Traulsen, Imke

[["dc.bibliographiccitation.artnumber","7512"],["dc.bibliographiccitation.issue","22"],["dc.bibliographiccitation.journal","Sensors"],["dc.bibliographiccitation.volume","21"],["dc.contributor.author","Wutke, Martin"],["dc.contributor.author","Heinrich, Felix"],["dc.contributor.author","Das, Pronaya Prosun"],["dc.contributor.author","Lange, Anita"],["dc.contributor.author","Gentz, Maria"],["dc.contributor.author","Traulsen, Imke"],["dc.contributor.author","Warns, Friederike K."],["dc.contributor.author","Schmitt, Armin Otto"],["dc.contributor.author","Gültas, Mehmet"],["dc.date.accessioned","2022-01-11T14:07:54Z"],["dc.date.available","2022-01-11T14:07:54Z"],["dc.date.issued","2021"],["dc.description.abstract","The identification of social interactions is of fundamental importance for animal behavioral studies, addressing numerous problems like investigating the influence of social hierarchical structures or the drivers of agonistic behavioral disorders. However, the majority of previous studies often rely on manual determination of the number and types of social encounters by direct observation which requires a large amount of personnel and economical efforts. To overcome this limitation and increase research efficiency and, thus, contribute to animal welfare in the long term, we propose in this study a framework for the automated identification of social contacts. In this framework, we apply a convolutional neural network (CNN) to detect the location and orientation of pigs within a video and track their movement trajectories over a period of time using a Kalman filter (KF) algorithm. Based on the tracking information, we automatically identify social contacts in the form of head–head and head–tail contacts. Moreover, by using the individual animal IDs, we construct a network of social contacts as the final output. We evaluated the performance of our framework based on two distinct test sets for pig detection and tracking. Consequently, we achieved a Sensitivity, Precision, and F1-score of 94.2%, 95.4%, and 95.1%, respectively, and a MOTA score of 94.4%. The findings of this study demonstrate the effectiveness of our keypoint-based tracking-by-detection strategy and can be applied to enhance animal monitoring systems."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.3390/s21227512"],["dc.identifier.pii","s21227512"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/97888"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-507"],["dc.relation.eissn","1424-8220"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Detecting Animal Contacts—A Deep Learning-Based Pig Detection and Tracking Approach for the Quantification of Social Contacts"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["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"]]