Event-Based Visual Flow

TLDR

Biological and artificial retinas are asynchronous, data‑driven systems that acquire light in a radically different way from conventional frame‑based cameras. The study aims to compute dense visual flow from the precise spike timings of an asynchronous event‑based retina. The authors propose a local differential framework that estimates visual flow from the spatiotemporal surface of coactive events. Experiments demonstrate microsecond‑accurate motion flow with very low computational cost, even under high data sparsity.

Abstract

This paper introduces a new methodology to compute dense visual flow using the precise timings of spikes from an asynchronous event-based retina. Biological retinas, and their artificial counterparts, are totally asynchronous and data-driven and rely on a paradigm of light acquisition radically different from most of the currently used frame-grabber technologies. This paper introduces a framework to estimate visual flow from the local properties of events' spatiotemporal space. We will show that precise visual flow orientation and amplitude can be estimated using a local differential approach on the surface defined by coactive events. Experimental results are presented; they show the method adequacy with high data sparseness and temporal resolution of event-based acquisition that allows the computation of motion flow with microsecond accuracy and at very low computational cost.