SENTRY: Spiking Event Reasoning for Selective Deep Inference in Event-Driven Edge Vision

Event-driven cameras are well suited for always-on edge vision, but forwarding all events creates unnecessary overhead. Events outside user-defined ROIs are semantically irrelevant, while many ROI events arise from background motion, lighting changes, or sensor noise rather than meaningful activity. We propose SENTRY, a near-sensor pipeline that addresses both issues through hierarchical spatial reasoning. A coarse spike-rate gate discards frames with low ROI activity, while a spatially-aware confirmation stage compares each region's activity against the global background rate to suppress diffuse noise. Evaluated on a real indoor surveillance sequence, SENTRY achieves +64% precision and - 57% false positive rate relative to a global spike-rate baseline, targeting resource-constrained, always-on platforms where energy efficiency and rapid response must be achieved simultaneously.