Animal Pose Estimation

Animal pose estimation aims to automatically identify and track the keypoints of animals in images and videos, enabling detailed behavioral analysis across diverse species. Current research emphasizes developing robust methods that handle limited labeled data, focusing on techniques like self-supervised learning, transfer learning from pre-trained models (e.g., using CNN architectures like EfficientNetV2), and leveraging synthetic data to augment scarce real-world datasets. These advancements are crucial for advancing ecological studies, veterinary medicine, and animal conservation by providing efficient and scalable tools for analyzing animal behavior and movement patterns.