Event based Multi-Velocity-Scale Imaging

Real-world scenes often contain static, slow, and fast components within the same field of view. Frame cameras are inherently inefficient in this setting, as their global exposure and fixed sampling must follow the fastest motion and therefore oversample the rest of the scene. Event cameras provide asynchronous local sensing, but standard sensors use a single global contrast threshold, leading to a trade-off between sensitivity and redundancy: low thresholds preserve static information but over-trigger under fast motion, while high thresholds reduce data volume but lose slow or static structures. We propose a multi-threshold event imaging model that assigns motion-compatible thresholds to different scene components, enabling faithful multi-velocity-scale imaging with fewer events. We also introduce a practical acquisition pipeline that implements this model on existing event cameras. Experiments on synthetic and real scenes validate the effectiveness of our approach in preserving both static structures and fast dynamics while reducing data volume.