VibES: Induced Vibration for Persistent Event-Based Sensing
This addresses a critical limitation for event-based vision systems in applications requiring continuous sensing, though it is incremental as it builds on prior motion-induced stimulation work.
The paper tackles the problem of event cameras failing to generate events in static or low-motion scenes by introducing a lightweight method using induced vibration to sustain persistent event generation, resulting in improved image reconstruction and edge detection.
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, becoming unsuitable for most computer vision tasks. To address this limitation, recent work has investigated motion-induced event stimulation that 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 demonstrate our approach with a hardware prototype and evaluate it on real-world captured datasets. Our method reliably recovers motion parameters and improves both image reconstruction and edge detection over event-based sensing without motion induction.