Retinal Slip Estimation and Object Tracking with an Active Event Camera

This paper presents a retinal slip estimation algorithm and a novel tracking strategy for active event-based cameras. Event-based sensors have independent, asynchronous pixels that report local luminance change. Unlike conventional image sensors, the event cameras have a high dynamic range and high temporal resolution. To preserve these advantages, we propose an algorithm that enables estimates of the normal component of the optical flow to be updated as spikes come in, eschewing the accumulation of events within a fixed temporal window. We also propose a method for integrating these normal flow estimates to estimate the retinal slip between the camera and object. We demonstrate the advantages of this asynchronous estimator of the retinal slip using an active event camera simulator (AESIM), which we developed to enable simulation of the active control of an event sensor in a dynamic environment.