Review of bioinspired real-time motion analysis systems

Abstract

Flying insects are able to manoeuvre through complex environments with remarkable ease and accuracy despite their simple visual system. Physiological evidence suggests that flight control is primarily guided by a small system of neurons tuned to very specific types of complex motion. This system is a promising model for bio-inspired approaches to low-cost artificial motion analysis systems, such as collision avoidance devices. A number of models of motion detection have been proposed, with the basic model being the Reichardt Correlator. Electrophysiological data suggest a variety of non-linear elaborations, which include compressive non-linearities and adaptive feedback of local motion detector outputs. In this paper we review a number of computational models for motion detection from the point of view of ease of implementation in low cost VLSI technology. We summarise the features of biological motion analysis systems that are important for the design of real-time artificial motion analysis systems. Then we report on recent progress in bio-inspired analog VLSI chips that capture properties of biological neural computation.