Man-made velocity estimators based on insect vision

Abstract

The study of insect vision is of significant interest to engineers for inspiring the design of future motion-sensitive smart sensor devices, for collision avoidance applications. Although insects are relatively simple organisms compared to vertebrates, they are blessed with a very efficient visual system, which enables them to navigate with great ease and accuracy. Biologically inspired motion detection models are bound to replace the conventional machine vision technology because of their simplicity and significant advantages in a number of applications. The dominant model for insect motion detection, first proposed by Hassentein and Reichardt in 1956, has gained widespread acceptance in the invertebrate vision community. The template model is another known model proposed later by Horridge in 1990, which permits simple tracking techniques and lends itself easily to both hardware and software. In this paper, we compare these two different motion detecting strategies. It was found from the data obtained from the intracellular recordings of the steady-state responses of wide-field neurons in the hoverfly Volucella, that the shape of the curves obtained agree with the theoretical predictions made by Dror. In order to compare this with the template model, we carried out an experiment to obtain the velocity response curves of the template model to the same image statistics. The results lead us to believe that the fly motion detector emulates a modified Reichardt correlator. In the second part of the paper, modifications are made to the Reichardt detector that improve its performance in velocity detection by reducing its dependance on contrast and image structure. Our recent neurobiological experiments suggest that adaptive mechanisms decrease the EMD (elementary motion detector) dependence on pattern contrast and improve reliability. So appropriate modelling of an adaptive feedback mechanism is carried out to normalize contrast of input signals in order to improve the reliability and robustness of velocity estimation.