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.
Abstract (translated)
URL
https://arxiv.org/abs/2206.12651
