Event cameras are a bio-inspired class of sensors that asynchronously measure per-pixel intensity changes. Under fixed illumination conditions in static or low-motion scenes, rigidly mounted event cameras are unable to generate any events and become unsuitable for most computer vision tasks. To address this limitation, recent work has investigated motion-induced event stimulation, which often requires complex hardware or additional optical components. In contrast, we introduce a lightweight approach to sustain persistent event generation by employing a simple rotating unbalanced mass to induce periodic vibrational motion. This is combined with a motion-compensation pipeline that removes the injected motion and yields clean, motion-corrected events for downstream perception tasks. We develop a hardware prototype to demonstrate our approach and evaluate it on real-world datasets. Our method reliably recovers motion parameters and improves both image reconstruction and edge detection compared to event-based sensing without motion induction.

翻译：事件相机是一类受生物启发的传感器，能够异步测量像素级的强度变化。在静态或低运动场景的固定光照条件下，刚性安装的事件相机无法生成任何事件，因此不适用于大多数计算机视觉任务。为解决这一限制，近期研究探索了运动诱导的事件激发方法，但通常需要复杂硬件或额外光学组件。相比之下，我们提出一种轻量化方法，通过采用简单的旋转不平衡质量块来产生周期性振动运动，从而实现持续的事件生成。该方法结合了运动补偿流程，可消除注入的运动干扰，为下游感知任务提供干净的运动校正事件。我们开发了硬件原型来验证该方法，并在真实数据集上进行评估。相较于无运动诱导的事件感知，我们的方法能够可靠地恢复运动参数，并显著提升图像重建与边缘检测的性能。