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dc.contributor.authorSuessle, Vanessa
dc.contributor.authorArandjelovic, Mimi
dc.contributor.authorKalan, Ammie K.
dc.contributor.authorAgbor, Anthony
dc.contributor.authorBoesch, Christophe
dc.contributor.authorBrazzola, Gregory
dc.contributor.authorDeschner, Tobias
dc.contributor.authorDieguez, Paula
dc.contributor.authorGranjon, Anne-Céline
dc.contributor.authorKuehl, Hjalmar
dc.contributor.authorLandsmann, Anja
dc.contributor.authorLapuente, Juan
dc.contributor.authorMaldonado, Nuria
dc.contributor.authorMeier, Amelia
dc.contributor.authorRockaiova, Zuzana
dc.contributor.authorWessling, Erin G.
dc.contributor.authorWittig, Roman M.
dc.contributor.authorDowns, Colleen T.
dc.contributor.authorWeinmann, Andreas
dc.contributor.authorHergenroether, Elke
dc.contributor.editorSkala, Václav
dc.date.accessioned2023-10-03T14:32:50Z
dc.date.available2023-10-03T14:32:50Z
dc.date.issued2023
dc.identifier.citationJournal of WSCG. 2023, vol. 31, no. 1-2, p. 1-10.en
dc.identifier.issn1213 – 6972 (hard copy)
dc.identifier.issn1213 – 6980 (CD-ROM)
dc.identifier.issn1213 – 6964 (on-line)
dc.identifier.urihttp://hdl.handle.net/11025/54279
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subjectindividuální identifikacecs
dc.subjectSIFT algoritmuscs
dc.subjectkonvoluční neuronové sítěcs
dc.subjectautomatická pipelinecs
dc.subjectshoda znakůcs
dc.subjectproblém s otevřenou množinoucs
dc.subjectzachování života v divočiněcs
dc.subjectfotopastics
dc.titleAutomatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Dataen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe manual processing and analysis of videos from camera traps is time-consuming and includes several steps, ranging from the filtering of falsely triggered footage to identifying and re-identifying individuals. In this study, we developed a pipeline to automatically analyze videos from camera traps to identify individuals without requiring manual interaction. This pipeline applies to animal species with uniquely identifiable fur patterns and solitary behavior, such as leopards (Panthera pardus). We assumed that the same individual was seen throughout one triggered video sequence. With this assumption, multiple images could be assigned to an individual for the initial database filling without pre-labeling. The pipeline was based on well-established components from computer vision and deep learning, particularly convolutional neural networks (CNNs) and scale-invariant feature transform (SIFT) features. We augmented this basis by implementing additional components to substitute otherwise required human interactions. Based on the similarity between frames from the video material, clusters were formed that represented individuals bypassing the open set problem of the unknown total population. The pipeline was tested on a dataset of leopard videos collected by the Pan African Programme: The Cultured Chimpanzee (PanAf) and achieved a success rate of over 83% for correct matches between previously unknown individuals. The proposed pipeline can become a valuable tool for future conservation projects based on camera trap data, reducing the work of manual analysis for individual identification, when labeled data is unavailable.en
dc.subject.translatedindividual identificationen
dc.subject.translatedSIFT algorithmen
dc.subject.translatedconvolutional neural networksen
dc.subject.translatedCNNen
dc.subject.translatedautomatic pipelineen
dc.subject.translatedpattern matchingen
dc.subject.translatedopen set problemen
dc.subject.translatedwildlife conservationen
dc.subject.translatedcamera trapsen
dc.identifier.doihttps://www.doi.org/10.24132/JWSCG.2023.1
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 31, Number 1-2 (2023)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 31 (2023)
  6. Volume 31, Number 1-2 (2023)