A Fast Biologically Inspired Algorithm for Recurrent Motion Estimation

We have previously developed a neurodynamical model of motion segregation in cortical visual area V1 and MT of the dorsal stream. The model explains how motion ambiguities caused by the motion aperture problem can be solved for coherently moving objects of arbitrary size by means of cortical mechanisms. The major bottleneck in the development of a reliable biologically inspired technical system with real-time motion analysis capabilities based on this neural model is the amount of memory necessary for the representation of neural activation in velocity space. We propose a sparse coding framework for neural motion activity patterns and suggest a means by which initial activities are detected efficiently. We realize neural mechanisms such as shunting inhibition and feedback modulation in the sparse framework to implement an efficient algorithmic version of our neural model of cortical motion segregation. We demonstrate that the algorithm behaves similarly to the original neural model and is able to extract image motion from real world image sequences. Our investigation transfers a neuroscience model of cortical motion computation to achieve technologically demanding constraints such as real-time performance and hardware implementation. In addition, the proposed biologically inspired algorithm provides a tool for modeling investigations to achieve acceptable simulation time