  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.
