Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-24927
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dc.contributor.authorVidondo, Beatriz-
dc.contributor.authorGlüge, Stefan-
dc.contributor.authorHubert, Laurtent-
dc.contributor.authorFischer, Claude-
dc.contributor.authorLe Grand, Luc-
dc.date.accessioned2022-05-06T13:32:28Z-
dc.date.available2022-05-06T13:32:28Z-
dc.date.issued2022-05-06-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/24927-
dc.description.abstractMotion-triggered camera traps are essential for the monitoring and management of wildlife. As per today in Switzerland, a high number of pictures is manually processed (annotated and classified). We study the utilization of available detection and classification models to (semi-)automatize this process. Two main aspects were investigated: 1) evaluate the feasibility of a non-expert local application (with Microsoft's MegaDetector model), and 2) quantify model performance using several labelled datasets of varying quality and content. Our results show a highly accurate (sensitive and specific), and reliable, fast inference which efficiently allows the automatic pre-discarding of all non-animal images. Further, the MegaDetector turns out to be both, user-friendly and highly performant and thus, an ideal tool for Swiss wildlife experts and stakeholders. Incentives (educational and financial) are required to promote knowledge transfer to this field.de_CH
dc.language.isoende_CH
dc.publisherZHAW Zürcher Hochschule für Angewandte Wissenschaftende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subjectObject detectionde_CH
dc.subjectComputer visionde_CH
dc.subject.ddc006: Spezielle Computerverfahrende_CH
dc.subject.ddc590: Tiere (Zoologie)de_CH
dc.titleAnimal detection and species classification on Swiss camera trap images using AIde_CH
dc.typeKonferenz: Posterde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Computational Life Sciences (ICLS)de_CH
dc.identifier.doi10.21256/zhaw-24927-
zhaw.conference.detailsBern Data Science Day (BDSD), Bern, 6 May 2022de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewPeer review (Abstract)de_CH
zhaw.webfeedPredictive Analyticsde_CH
zhaw.author.additionalNode_CH
zhaw.display.portraitNode_CH
Appears in collections:Publikationen Life Sciences und Facility Management

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Vidondo, B., Glüge, S., Hubert, L., Fischer, C., & Le Grand, L. (2022, May 6). Animal detection and species classification on Swiss camera trap images using AI. Bern Data Science Day (BDSD), Bern, 6 May 2022. https://doi.org/10.21256/zhaw-24927
Vidondo, B. et al. (2022) ‘Animal detection and species classification on Swiss camera trap images using AI’, in Bern Data Science Day (BDSD), Bern, 6 May 2022. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. Available at: https://doi.org/10.21256/zhaw-24927.
B. Vidondo, S. Glüge, L. Hubert, C. Fischer, and L. Le Grand, “Animal detection and species classification on Swiss camera trap images using AI,” in Bern Data Science Day (BDSD), Bern, 6 May 2022, May 2022. doi: 10.21256/zhaw-24927.
VIDONDO, Beatriz, Stefan GLÜGE, Laurtent HUBERT, Claude FISCHER und Luc LE GRAND, 2022. Animal detection and species classification on Swiss camera trap images using AI. In: Bern Data Science Day (BDSD), Bern, 6 May 2022. Conference poster. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. 6 Mai 2022
Vidondo, Beatriz, Stefan Glüge, Laurtent Hubert, Claude Fischer, and Luc Le Grand. 2022. “Animal Detection and Species Classification on Swiss Camera Trap Images Using AI.” Conference poster. In Bern Data Science Day (BDSD), Bern, 6 May 2022. ZHAW Zürcher Hochschule für Angewandte Wissenschaften. https://doi.org/10.21256/zhaw-24927.
Vidondo, Beatriz, et al. “Animal Detection and Species Classification on Swiss Camera Trap Images Using AI.” Bern Data Science Day (BDSD), Bern, 6 May 2022, ZHAW Zürcher Hochschule für Angewandte Wissenschaften, 2022, https://doi.org/10.21256/zhaw-24927.


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