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Image-based activity monitoring of pigs

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dc.contributor.authorJan-Hendrik Witte, Jorge Marx Gómez
dc.date.accessioned2024-04-08T11:56:33Z
dc.date.available2024-04-08T11:56:33Z
dc.date.issued2024
dc.description.abstractIn modern pig livestock farming, animal well-being is of paramount importance. Monitoring activity is crucial for early detection of potential health or behavioral anomalies. Traditional object tracking methods such as DeepSort often falter due to the pigs' similar appearances, frequent overlaps, and close-proximity movements, making consistent long-term tracking challenging. To address this, our study presents a novel methodology that eliminates the need for conventional tracking to capture activity on pen-level. Instead, we segment video frames into predefined sectors, where pig postures are determined using YOLOv8 for pig detection and EfficientNetV2 for posture classification. Activity levels are then assessed by comparing sector counts between consecutive frames. Preliminary results indicate discernible variations in pig activity throughout the day, highlighting the efficacy of our method in capturing activity patterns. While promising, this approach remains a proof of concept, and its practical implications for real-world agricultural settings warrant further investigation.en
dc.identifier.doi10.18420/giljt2024_05
dc.identifier.isbn978-3-88579-738-8
dc.identifier.issn2944-7682
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43868
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics(LNI) - Proceedings, Volume P - 344
dc.subjectprecision livestock farming
dc.subjectdeep learning
dc.subjectcomputer vision
dc.subjectactivity monitoring
dc.titleImage-based activity monitoring of pigsen
dc.typeText/Conference Paper
gi.citation.endPage178
gi.citation.publisherPlaceBonn
gi.citation.startPage167
gi.conference.date27.-28. Februar 2024
gi.conference.locationStuttgart
gi.conference.reviewfull

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