Great Ape

Great ape research is increasingly leveraging advanced computer vision and machine learning techniques to analyze large video and audio datasets, aiming to improve behavioral understanding and conservation efforts. Current research focuses on developing robust models, including convolutional neural networks (CNNs), vision transformers, and recurrent neural networks (RNNs), for tasks such as automated behavior recognition, species identification, and pose estimation. These advancements are crucial for efficiently analyzing vast amounts of data collected through camera traps and other monitoring methods, ultimately contributing to more accurate assessments of great ape populations, distribution, and behavior in the wild. The development of high-quality annotated datasets is also a key focus, addressing challenges like annotation inconsistencies and the need for large, diverse datasets representing various ape species and environments.