Review on Social Behavior Analysis of Laboratory Animals: From Methodologies to Applications

TL;DR

This paper reviews various computer vision techniques, including traditional, statistical, and deep learning methods, for automatic animal behavior detection to assist biologists in efficient data analysis.

Contribution

It provides a comprehensive survey of existing animal behavior detection algorithms and discusses their strengths and weaknesses.

Findings

Deep learning methods show promising accuracy.

Traditional methods are less robust but computationally simpler.

The survey highlights gaps for future research.

Abstract

As the bridge between genetic and physiological aspects, animal behaviour analysis is one of the most significant topics in biology and ecological research. However, identifying, tracking and recording animal behaviour are labour intensive works that require professional knowledge. To mitigate the spend for annotating data, researchers turn to computer vision techniques for automatic label algorithms, since most of the data are recorded visually. In this work, we explore a variety of behaviour detection algorithms, covering traditional vision methods, statistical methods and deep learning methods. The objective of this work is to provide a thorough investigation of related work, furnishing biologists with a scratch of efficient animal behaviour detection methods. Apart from that, we also discuss the strengths and weaknesses of those algorithms to provide some insights for those who already delve into this field.