Automatic Individual Identification of Patterned Solitary Species Based on Unlabeled Video Data

• DOI: 10.24132/jwscg.2023.1
• Publication Date: 2023-07-19T05:58:22Z
• Authors: Vanessa Suessle; Mimi Arandjelovic; ; ; Ammie K. Kalan; ; Anthony Agbor; ; Christophe Boesch; ; Gregory Brazzola; ; Tobias Deschner; ; Paula Dieguez; ; Anne-Céline Granjon; ; Hjalmar Kuehl; ; ; ; Anja Landsmann; ; Juan Lapuente; ; Nuria Maldonado; ; Amelia Meier; ; Zuzana Rockaiova; ; Erin G. Wessling; ; ; Roman M. Wittig; ; ; Colleen T. Downs; ; Andreas Weinmann; ; Elke Hergenroether
• Keywords: FOS: Computer and information sciences; individuální identifikace; fotopasti; Computer Vision and Pattern Recognition (cs.CV); zachování života v divočině; automatic pipeline; Computer Science - Computer Vision and Pattern Recognition; individual identification; SIFT algorithm; automatická pipeline; 01 natural sciences; konvoluční neuronové sítě; shoda znaků; pattern matching; camera traps; open set problem; convolutional neural networks; wildlife conservation; 0103 physical sciences; SIFT algoritmus; CNN; problém s otevřenou množinou

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.