Static in Frames, Dynamic in Events: Rethinking Features in Event Cameras as Motion Cues

2026-08-11

AI Summary

This work addresses the performance limitations in event-based motion estimation tasks, such as optical flow, which stem from the lack of effective feature representations capturing the dynamic nature of event camera data. The study reveals, for the first time, that the eigenvalues of the structure tensor commonly used in classical corner detection inherently encode motion cues. Building on this insight, the authors propose a lightweight and robust feature representation that integrates spatiotemporal event density with local geometric information. This representation is seamlessly incorporated into an event-based optical flow network, yielding substantial improvements in estimation accuracy on the real-world DSEC dataset. Notably, the approach excels under challenging conditions—including small model capacity and limited training data—and demonstrates strong robustness to texture variations and shot noise.

Abstract

Event cameras capture intensity changes asynchronously with high temporal resolution, requiring novel preprocessing methods for downstream tasks. Unlike static intensity snapshots, event data inherently encode information about scene dynamics and object motion, meaning that features derived from events can exhibit behaviors with no direct analogue in frame-based vision. In this paper, we analyze two features used in event-based corner detection---the eigenvalues of the structure tensor and the spatiotemporal density values---and show that they are \emph{motion cues}. We hypothesize that these features, combined with local geometric information, can enhance motion estimation tasks. To validate this, we first theoretically analyze how the eigenvalues of the structure tensor at moving corner points relate to the direction of motion. We then design controlled experiments on a synthetic dataset, confirming that extending local geometric features with eigenvalues and density values provides complementary motion information and is robust to texture and shot noise. Finally, we integrate the proposed features into a state-of-the-art event-based optical flow network and evaluate on the real-world DSEC benchmark, where the added features consistently improve accuracy, with the largest gains in data-scarce scenarios and for lower-capacity models. The code for this paper can be found at: \href{https://github.com/hesamaraghi/static-in-frames-dynamic-in-events}{https://github.com/hesamaraghi/static-in-frames-dynamic-in-events}.

Problem

• event cameras
• motion cues
• structure tensor
• optical flow
• feature representation

Innovation

• event cameras
• motion cues
• structure tensor
• spatiotemporal density
• optical flow