Bina-Rep Event Frames: a Simple and Effective Representation for Event-based cameras

TL;DR

Bina-Rep converts asynchronous event streams from event cameras into sparse, expressive frames using N-bit encoding, improving performance and robustness in event-based vision tasks.

Contribution

The paper introduces Bina-Rep, a novel N-bit encoding method for event frames that enhances expressiveness and performance over existing representations.

Findings

Achieves state-of-the-art performance in event-based tasks.

Produces sparser, more expressive event frames.

Demonstrates robustness against image corruptions.

Abstract

This paper presents "Bina-Rep", a simple representation method that converts asynchronous streams of events from event cameras to a sequence of sparse and expressive event frames. By representing multiple binary event images as a single frame of $N$-bit numbers, our method is able to obtain sparser and more expressive event frames thanks to the retained information about event orders in the original stream. Coupled with our proposed model based on a convolutional neural network, the reported results achieve state-of-the-art performance and repeatedly outperforms other common event representation methods. Our approach also shows competitive robustness against common image corruptions, compared to other representation techniques.