An Event-by-Event Approach for Velocity Estimation and Object Tracking With an Active Event Camera

An event-based camera has independent pixels that quickly respond to relative brightness changes and report them asynchronously with high temporal resolution (microsecond level). We present an event-by-event processing framework for sensing the velocity of a moving object and tracking it with an active event-based camera. This paper describes an event-by-event approach for estimating both local velocity and global velocity. Instead of accumulating events over a fixed period of time, we directly estimate the normal velocity of the pixel at which a new event occurs and update the estimate of the global velocity given the new estimate. Our proposed global velocity estimation algorithm is based on the Kalman filter which also provides a confidence measurement for the estimated velocity. Our active tracking system monitors the confidence of the velocity estimate and triggers a tracking command once the confidence reaches a certain threshold. We evaluated our velocity estimation algorithms and the active tracking system in our recently proposed simulator, AESIM. Our results demonstrate that our system can successfully track a moving object with low latency.