The field of wildlife conservation is increasingly leveraging computer vision to monitor and track animal populations. Recent developments have focused on improving the accuracy and efficiency of species identification, object detection, and image classification. A key direction in this field is the creation of large-scale datasets for training and evaluating machine learning models, such as those for identifying species from indirect evidence like footprints and feces. Another area of innovation is the application of deep learning models for image classification, including the use of transfer learning and vision transformers. Noteworthy papers in this area include:

• AnimalClue, which introduces a large-scale dataset for species identification from images of indirect evidence, and
• Evaluating Deep Learning Models for African Wildlife Image Classification, which presents a comparative study of deep learning models for automatically classifying African wildlife images. These advancements have the potential to significantly improve the efficiency and effectiveness of wildlife conservation efforts.