Biologically-inspired robust motion segmentation using mutual information

•Presents novel neuroscience inspired information theoretic approach to motion segmentation based on mutual information.•New model of current findings in biological vision is presented and link established to existing motion segmentation algorithms.•Comparative performance evaluation across four challenging datasets.•Comparative performance evaluation against competing segmentation methods.

This paper presents a neuroscience inspired information theoretic approach to motion segmentation. Robust motion segmentation represents a fundamental first stage in many surveillance tasks. As an alternative to widely adopted individual segmentation approaches, which are challenged in different ways by imagery exhibiting a wide range of environmental variation and irrelevant motion, this paper presents a new biologically-inspired approach which computes the multivariate mutual information between multiple complementary motion segmentation outputs. Performance evaluation across a range of datasets and against competing segmentation methods demonstrates robust performance.