%0 Journal Article %A Suessle, Vanessa %A Arandjelovic, Milica %A Kalan, Ammie K. %A Agbor, Anthony %A Boesch, Christophe %A Brazzola, Gregory %A Deschner, Tobias %A Dieguez, Paula %A Granjon, Anne-Céline %A Kühl, Hjalmar S. %A Landsmann, Anja %A Lapuente, Juan %A Maldonado, Nuria %A Meier, Amelia %A Rockaiova, Zuzana %A Wessling, Erin G. %A Wittig, Roman M. %A Downs, Colleen T. %A Weinmann, Andreas %A Hergenroether, Elke %+ Department of Primate Behavior and Evolution, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Chimpanzees, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Chimpanzees, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Gorillas, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Chimpanzees, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society Great Ape Evolutionary Ecology and Conservation, Department of Primatology, Max Planck Institute for Evolutionary Anthropology, Max Planck Society %T Automatic individual identification of patterned solitary species based on unlabeled video data : %G eng %U https://hdl.handle.net/21.11116/0000-000D-8CE9-F %R 10.24132/JWSCG.2023.1 %7 2023-07 %D 2023 %X The 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. %K individual identification, SIFT algorithm, CNNs, automatic pipeline, pattern matching, open set problem, wildlife conservation, camera traps %J Journal of WSCG %V 31 %& 1 %P 1 - 10 %I Vaclav Skala - Union Agency %C Plzen %@ 1213-69721213-6964