通过自适应调节传感器参数，有效抑制光照闪烁对事件相机的干扰。
Autobiasing Event Cameras for Flickering Mitigation
- 利用CNN检测空间闪烁并动态调整相机偏置
- 光照充足时梯度下降38.2%，低光下下降53.6%
- 提升目标检测准确率，适合复杂光照场景
理解并缓解由光照强度快速变化引起的闪烁效应，对于提升事件相机在多样环境中的性能至关重要。本文提出一种创新的自主机制，通过调节事件相机的内部偏置设置，有效应对25 Hz至500 Hz范围内的闪烁问题。不同于依赖额外硬件或软件滤波的传统方法，本方案充分利用事件相机自身的偏置可调特性。基于简单的卷积神经网络（CNN），系统可在空间域识别闪烁现象，并动态调整特定偏置以最小化其影响。该自适应偏置系统在多种光照条件下（包括明亮与低光）及不同频率下，通过人脸检测框架进行了充分验证。结果显示：人脸检测的YOLO置信度显著提升，捕获到目标的帧数比例增加；同时，作为闪烁指示的平均梯度在光照充足条件下降低38.2%，低光条件下降低53.6%。这些结果表明，该方法能显著增强事件相机在复杂照明环境下的实用性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。