Frequency-domain Event-based Imaging for Selective Surveillance

• URL: http://arxiv.org/abs/2605.15392v1
• Date: Thu, 14 May 2026 20:25:51 GMT
• Title: Frequency-domain Event-based Imaging for Selective Surveillance
• Abstract summary: We introduce Frequency Rate Information for Event Space (FRIES), a neuromorphic processing framework.<n>FRIES detects periodicity in events, such as rotor rotation and mechanical vibrations, to discriminate and monitor man-made objects.<n>Preliminary results establish frequency-domain event processing as a promising front-end for selective surveillance in neuromorphic pipelines.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Event-based cameras (EBCs) are an attractive sensing modality for surveillance due to their reporting of pixel-level radiance changes with microsecond resolution and high dynamic range, enabling motion extraction while suppressing background. Their asynchronous, sparse output, however, necessitate algorithms that identify targets in event-space without processing full frames. We introduce Frequency Rate Information for Event Space (FRIES), a neuromorphic processing framework that detects periodicity in events, such as rotor rotation and mechanical vibrations, to discriminate and monitor man-made objects. FRIES first applies a time gate to suppress background and noise, then aggregates events into a pixel-wise activity (e.g., density) map and clusters pixels into regions-of-interest (ROIs). A localized spectral analysis is applied to each ROI to extract dominant frequencies used to distinguish structured object signatures from unstructured background and noise. Discriminated targets are visualized using a Resonant Time Surface (RTS), a frequency-selective method that weights events by their phase coherence with the extracted frequencies, rewarding in-sync content and suppressing out-of-sync clutter. We demonstrate FRIES and RTS in a controlled indoor experiment to recover the rotational frequency of a mechanical chopper and drone rotors against a moving background. We further test these methods on an outdoor data to detect a hovering drone against a realistic treeline. These preliminary results establish frequency-domain event processing as a promising front-end for selective surveillance in neuromorphic pipelines and a complementary surveillance modality, leveraging the high temporal resolution to enable spectral discrimination.

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