Asynchronous, Photometric Feature Tracking using Events and Frames

TL;DR

This paper introduces a novel asynchronous feature tracking method that combines event cameras and standard frames, leveraging their complementary strengths to achieve high-accuracy, low-latency visual tracking.

Contribution

It presents the first principled, generative model-based approach for directly using raw intensity measurements from combined event and frame data.

Findings

More accurate feature tracks than previous methods

Longer feature tracks across diverse scenes

Subpixel accuracy in feature tracking

Abstract

We present a method that leverages the complementarity of event cameras and standard cameras to track visual features with low-latency. Event cameras are novel sensors that output pixel-level brightness changes, called "events". They offer significant advantages over standard cameras, namely a very high dynamic range, no motion blur, and a latency in the order of microseconds. However, because the same scene pattern can produce different events depending on the motion direction, establishing event correspondences across time is challenging. By contrast, standard cameras provide intensity measurements (frames) that do not depend on motion direction. Our method extracts features on frames and subsequently tracks them asynchronously using events, thereby exploiting the best of both types of data: the frames provide a photometric representation that does not depend on motion direction and the events provide low-latency updates. In contrast to previous works, which are based on heuristics, this is the first principled method that uses raw intensity measurements directly, based on a generative event model within a maximum-likelihood framework. As a result, our method produces feature tracks that are both more accurate (subpixel accuracy) and longer than the state of the art, across a wide variety of scenes.