Frequency-domain Event-based Imaging for Selective Surveillance

2026-05-14

AI Summary

This work addresses the challenge of identifying periodic artificial targets from the asynchronous, sparse event streams generated by event cameras. To this end, the authors propose the FRIES framework, which employs temporal gating to suppress background noise, combines pixel-wise activity maps with region clustering to generate regions of interest, and performs local spectral analysis on each region to extract dominant frequencies. Furthermore, a Resonant Time Surface (RTS) is introduced to enable frequency-selective visualization, enhancing events whose phases align with the target frequency. This study pioneers the application of frequency-domain analysis to event-based vision, leveraging the high temporal resolution of event streams for periodicity-driven target discrimination and selective monitoring. Experiments demonstrate accurate recovery of mechanical chopper and drone rotor frequencies indoors, as well as effective detection of hovering drones against complex outdoor foliage backgrounds.

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.

Problem

• event-based cameras
• selective surveillance
• frequency-domain processing
• periodic motion detection
• neuromorphic vision

Innovation

• event-based vision
• frequency-domain analysis
• neuromorphic computing
• selective surveillance
• Resonant Time Surface