Autobiasing Event Cameras for Flickering Mitigation

TL;DR

This paper presents an autonomous bias tuning system for event cameras that reduces flicker effects across a wide frequency range, improving detection performance and image stability without additional hardware.

Contribution

The novel autobiasing mechanism uses CNNs to dynamically adjust camera biases, effectively mitigating flicker in diverse lighting conditions and frequencies.

Findings

Significant increase in face detection confidence with YOLO.

38.2% and 53.6% reduction in flicker indicator in different lighting.

Improved frame capture rate of detected faces.

Abstract

Understanding and mitigating flicker effects caused by rapid variations in light intensity is critical for enhancing the performance of event cameras in diverse environments. This paper introduces an innovative autonomous mechanism for tuning the biases of event cameras, effectively addressing flicker across a wide frequency range -25 Hz to 500 Hz. Unlike traditional methods that rely on additional hardware or software for flicker filtering, our approach leverages the event cameras inherent bias settings. Utilizing a simple Convolutional Neural Networks -CNNs, the system identifies instances of flicker in a spatial space and dynamically adjusts specific biases to minimize its impact. The efficacy of this autobiasing system was robustly tested using a face detector framework under both well-lit and low-light conditions, as well as across various frequencies. The results demonstrated significant improvements: enhanced YOLO confidence metrics for face detection, and an increased percentage of frames capturing detected faces. Moreover, the average gradient, which serves as an indicator of flicker presence through edge detection, decreased by 38.2 percent in well-lit conditions and by 53.6 percent in low-light conditions. These findings underscore the potential of our approach to significantly improve the functionality of event cameras in a range of adverse lighting scenarios.