Beyond observation: Deep learning for animal behavior and ecological conservation

Abstract

Recent advancements in deep learning have profoundly impacted the field of animal behavioral research, offering researchers powerful tools for understanding the complexities of animal movements and cognition. This comprehensive review is dedicated to an in-depth examination of the latest techniques, tools, and applications of deep learning in this domain. This study examines the principles of deep-learning-based tracking, pose estimation, and behavioral analysis, emphasizing their respective strengths, limitations, and practical implementation. From markerless pose tracking to multi-animal behavior classification, we present a variety of methodologies that facilitate high-throughput and precise behavioral quantification across diverse species and settings. Furthermore, emerging trends, such as the integration of drones and computer vision for the study of group dynamics in natural environments, as well as advancements in semi-supervised and unsupervised learning for robust behavioral segmentation and classification, were also examined. Given the pivotal role of responsible research, we address the pivotal challenges of scalability, robustness, and ethical considerations, paving the way for future research. By synthesizing insights from seminal works in neuroscience, computer vision, and artificial intelligence, this study provides researchers with a comprehensive understanding of the powerful tools and methodologies available to unlock the secrets of animal behavior and make promising discoveries across the vast animal kingdom.