Review on Social Behavior Analysis of Laboratory Animals: From Methodologies to Applications
It addresses the problem of reducing annotation costs for biologists studying animal behavior, but it is incremental as it synthesizes existing work.
This paper reviews computer vision algorithms for automating the labor-intensive task of animal behavior analysis, covering traditional, statistical, and deep learning methods to assist biologists.
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.