Autobiasing Event Cameras for Flickering Mitigation

2025-11-04

AI Summary

Event cameras suffer from flicker-induced performance degradation under strobed illumination (25–500 Hz), where rapid intensity variations cause spurious event generation. To address this, we propose a real-time flicker suppression method that autonomously adapts the camera’s internal bias parameters—leveraging the event camera’s native programmable bias configuration without requiring additional hardware or post-processing filters. A lightweight CNN processes event streams online to identify spatial flicker patterns and dynamically optimize bias settings. Evaluated on YOLO-based face detection, our method significantly improves detection confidence and frame rate: edge gradient error decreases by 38.2% under bright illumination and by 53.6% in low-light conditions. These results demonstrate robustness and effectiveness across diverse lighting scenarios.

Technology Category

• Computer Vision: Bias, Fairness & Privacy
• Machine Learning: Learning on the Edge & Model Compression
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Mitigating flicker effects from light intensity variations in event cameras
• Autonomously tuning camera biases to address flicker across 25-500 Hz range
• Improving event camera performance in adverse lighting conditions without additional hardware

Innovation

• Autonomous bias tuning mechanism for flicker mitigation
• CNN-based flicker detection in spatial domain
• Dynamic bias adjustment without additional hardware