Animal Identification

Animal identification research focuses on developing robust and accurate methods for automatically recognizing individual animals from visual data, aiding ecological monitoring and conservation efforts. Current approaches leverage deep learning models, often employing transfer learning from pre-trained architectures like ResNet and VGG, and incorporating techniques such as similarity fusion, background/foreground modeling, and 3D model fitting to improve accuracy and handle challenges like occlusion and viewpoint variation. Large-scale datasets, like PetFace, are crucial for training and benchmarking these models, while the exploration of alternative data modalities, such as hyperspectral imaging and RFID tags, expands the scope of identification possibilities. These advancements are significantly impacting wildlife management and research by enabling efficient, large-scale monitoring and behavioral analysis.