Název:	Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data
Autoři:	Suessle, Vanessa Arandjelovic, Mimi Kalan, Ammie K. Agbor, Anthony Boesch, Christophe Brazzola, Gregory Deschner, Tobias Dieguez, Paula Granjon, Anne-Céline Kuehl, Hjalmar Landsmann, Anja Lapuente, Juan Maldonado, Nuria Meier, Amelia Rockaiova, Zuzana Wessling, Erin G. Wittig, Roman M. Downs, Colleen T. Weinmann, Andreas Hergenroether, Elke
Citace zdrojového dokumentu:	Journal of WSCG. 2023, vol. 31, no. 1-2, p. 1-10.
Datum vydání:	2023
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	článek article
URI:	http://hdl.handle.net/11025/54279
ISSN:	1213 – 6972 (hard copy) 1213 – 6980 (CD-ROM) 1213 – 6964 (on-line)
Klíčová slova:	individuální identifikace;SIFT algoritmus;konvoluční neuronové sítě;automatická pipeline;shoda znaků;problém s otevřenou množinou;zachování života v divočině;fotopasti
Klíčová slova v dalším jazyce:	individual identification;SIFT algorithm;convolutional neural networks;CNN;automatic pipeline;pattern matching;open set problem;wildlife conservation;camera traps
Abstrakt v dalším jazyce:	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.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	Volume 31, Number 1-2 (2023)

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