Active Event-Based Static Perception Through Bio-Inspired Blinking Under Extreme Lighting Conditions

Event cameras offer exceptionally high temporal resolution and dynamic range, enabling robust capture of scene changes even under low-light conditions. However, their inability to generate events in static scenes fundamentally limits their applicability to tasks such as SLAM, object tracking, and recognition. We propose Robo-Eyelids, a bio-inspired system employing a mechanical shutter to mimic artificial “blinking”. A Random Temporal-Spatio Decay (RTSD) algorithm detects scene stationarity to trigger blinking, producing full-frame event streams, while a formula-based intensity reconstruction generates corresponding intensity images. Furthermore, a Peripheral Trilinear Event Double Integral (Peripheral-TEDI) model fuses APS frames with event data to dynamically select the optimal frame. In static environments, Robo-Eyelids achieves continuous high-quality intensity images at 15 fps under strong light and 8 fps under low light, mitigating the limitations of event cameras in static perception and demonstrating robust performance in downstream tasks such as optical flow tracking and object detection.