Lifetime estimation of events from Dynamic Vision Sensors

TLDR

A Dynamic Vision Sensor transmits pixel‑level brightness changes at micro‑second resolution, offering low latency and sparse output that makes it promising for high‑speed mobile robotics. The study proposes an algorithm to estimate the lifetime of events from Dynamic Vision Sensors. The algorithm augments each event with a lifetime derived from its image‑plane velocity, enabling the construction of sharp gradient images at any instant. The augmented event stream provides a continuous temporal representation that outperforms fixed‑interval accumulation, as demonstrated in high‑speed quadrotor flips and compared to standard visualization methods.

Abstract

We propose an algorithm to estimate the “lifetime” of events from retinal cameras, such as a Dynamic Vision Sensor (DVS). Unlike standard CMOS cameras, a DVS only transmits pixel-level brightness changes (“events”) at the time they occur with micro-second resolution. Due to its low latency and sparse output, this sensor is very promising for high-speed mobile robotic applications. We develop an algorithm that augments each event with its lifetime, which is computed from the event's velocity on the image plane. The generated stream of augmented events gives a continuous representation of events in time, hence enabling the design of new algorithms that outperform those based on the accumulation of events over fixed, artificially-chosen time intervals. A direct application of this augmented stream is the construction of sharp gradient (edge-like) images at any time instant. We successfully demonstrate our method in different scenarios, including high-speed quadrotor flips, and compare it to standard visualization methods.