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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorSalah, Albert
dc.contributor.authorBartolomeo, Francesco De
dc.date.accessioned2025-08-21T01:02:03Z
dc.date.available2025-08-21T01:02:03Z
dc.date.issued2025
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/49914
dc.description.abstractActive monitoring is indispensable in containing the spread of contagious diseases among animal stocks, particularly in detecting any intruders that may introduce harmful pathogens into livestock farms. Given the time-consuming nature of video monitoring and human attention limitations, there is a need for autonomous systems capable of continuously screening videos from the moment the camera starts recording. In this thesis, we utilize Deep Learning object detectors coupled with object trackers which can significantly automate these tasks reducing human intervention.
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectAutomatic rodent detection and tracking from motion-triggered wildlife cameras capturing activity mostly at night using object detectors and trackers within computer vision domain.
dc.titleAutomatic Rodent Detection from Motion-triggered Wildlife Cameras
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsObject detection; Object trackers; Animal monitoring;
dc.subject.courseuuArtificial Intelligence
dc.thesis.id51958


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