  Motion perception is a critical capability determining a variety of aspects
of insects' life, including avoiding predators, foraging and so forth. A good
number of motion detectors have been identified in the insects' visual
pathways. Computational modelling of these motion detectors has not only been
providing effective solutions to artificial intelligence, but also benefiting
the understanding of complicated biological visual systems. These biological
mechanisms through millions of years of evolutionary development will have
formed solid modules for constructing dynamic vision systems for future
intelligent machines. This article reviews the computational motion perception
models originating from biological research of insects' visual systems in the
literature. These motion perception models or neural networks comprise the
looming sensitive neuronal models of lobula giant movement detectors (LGMDs) in
locusts, the translation sensitive neural systems of direction selective
neurons (DSNs) in fruit flies, bees and locusts, as well as the small target
motion detectors (STMDs) in dragonflies and hover flies. We also review the
applications of these models to robots and vehicles. Through these modelling
studies, we summarise the methodologies that generate different direction and
size selectivity in motion perception. At last, we discuss about multiple
systems integration and hardware realisation of these bio-inspired motion
perception models.
