dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Salah, Albert | |
dc.contributor.author | Bartolomeo, Francesco De | |
dc.date.accessioned | 2025-08-21T01:02:03Z | |
dc.date.available | 2025-08-21T01:02:03Z | |
dc.date.issued | 2025 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/49914 | |
dc.description.abstract | Active 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.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | Automatic rodent detection and tracking from motion-triggered wildlife cameras capturing activity mostly at night using object detectors and trackers within computer vision domain. | |
dc.title | Automatic Rodent Detection from Motion-triggered Wildlife Cameras | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | Object detection; Object trackers; Animal monitoring; | |
dc.subject.courseuu | Artificial Intelligence | |
dc.thesis.id | 51958 | |