Animal Behavior Analysis Methods Using Deep Learning: A Survey

TL;DR

This survey reviews deep learning techniques for animal behavior analysis, highlighting current architectures, datasets, challenges, and future research directions to enhance understanding of animal health and social dynamics.

Contribution

It provides a comprehensive overview of deep learning applications in animal behavior analysis, including architectures, datasets, challenges, and future research pathways.

Findings

Deep learning models show high accuracy in classifying animal behaviors.

Limited adoption of deep learning in animal behavior studies persists.

Key challenges include dataset scarcity and variability.

Abstract

Animal behavior serves as a reliable indicator of the adaptation of organisms to their environment and their overall well-being. Through rigorous observation of animal actions and interactions, researchers and observers can glean valuable insights into diverse facets of their lives, encompassing health, social dynamics, ecological relationships, and neuroethological dimensions. Although state-of-the-art deep learning models have demonstrated remarkable accuracy in classifying various forms of animal data, their adoption in animal behavior studies remains limited. This survey article endeavors to comprehensively explore deep learning architectures and strategies applied to the identification of animal behavior, spanning auditory, visual, and audiovisual methodologies. Furthermore, the manuscript scrutinizes extant animal behavior datasets, offering a detailed examination of the principal challenges confronting this research domain. The article culminates in a comprehensive discussion of key research directions within deep learning that hold potential for advancing the field of animal behavior studies.