{"paper":{"title":"Frequency-domain Event-based Imaging for Selective Surveillance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Frequency analysis of event data from event-based cameras isolates rotating man-made objects like drone rotors from background clutter.","cross_cats":["cs.CV"],"primary_cat":"physics.optics","authors_text":"Adrish Kar, James Rick, Jason Zutty, Joseph L. Greene, Megan Birch","submitted_at":"2026-05-14T20:25:51Z","abstract_excerpt":"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-ma"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"FRIES applies time gating, pixel-wise activity mapping, ROI clustering, and localized spectral analysis to extract dominant frequencies that distinguish structured object signatures from unstructured background and noise; RTS then weights events by phase coherence with those frequencies.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That periodicity from man-made objects such as rotor rotation produces detectable dominant frequencies in the aggregated event data of each ROI that can be reliably separated from background and noise without full-frame processing.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Introduces FRIES framework and RTS visualization to detect periodic event signatures from event cameras for discriminating man-made objects against background.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Frequency analysis of event data from event-based cameras isolates rotating man-made objects like drone rotors from background clutter.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"8449c961c405e02263bcdf0ecae8181f4dd06b6dcaa3a06bfd9112609e7918da"},"source":{"id":"2605.15392","kind":"arxiv","version":1},"verdict":{"id":"2ec7cfb8-5711-4025-ac29-c81c5e0162c1","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-19T15:38:52.626151Z","strongest_claim":"FRIES applies time gating, pixel-wise activity mapping, ROI clustering, and localized spectral analysis to extract dominant frequencies that distinguish structured object signatures from unstructured background and noise; RTS then weights events by phase coherence with those frequencies.","one_line_summary":"Introduces FRIES framework and RTS visualization to detect periodic event signatures from event cameras for discriminating man-made objects against background.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That periodicity from man-made objects such as rotor rotation produces detectable dominant frequencies in the aggregated event data of each ROI that can be reliably separated from background and noise without full-frame processing.","pith_extraction_headline":"Frequency analysis of event data from event-based cameras isolates rotating man-made objects like drone rotors from background clutter."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.15392/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-19T16:01:18.026007Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T15:53:37.016025Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"citation_quote_validity","ran_at":"2026-05-19T15:50:56.619893Z","status":"completed","version":"0.1.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-19T14:21:54.168663Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T13:33:22.721980Z","status":"skipped","version":"1.0.0","findings_count":0}],"snapshot_sha256":"9f8e669933021216749b35a612d2a3e50f33bf3a2c3355363774160dfa0d82cc"},"references":{"count":30,"sample":[{"doi":"","year":2022,"title":"Situational Awareness: Techniques, Challenges, and Prospects,","work_id":"e6365675-999b-4a40-84dc-7e711b3c2d8e","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Event-Based Vision: A Survey,","work_id":"f166f0cf-0ab4-4470-82a5-e4b202450406","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Learning Event-Based Motion Deblurring,","work_id":"6608f197-7458-45b1-ae16-ee8d6e24146f","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars,","work_id":"7cb7c8c1-1cbf-49f0-b91e-75c5bf3c3d86","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Turbulence mitigation in imagery including moving objects from a static event camera,","work_id":"43658904-1951-45df-bb6a-b193a05ba5c4","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":30,"snapshot_sha256":"2134c45efaf740296ab8205438c7eed9d6e2e4f3e73d5bf9c2308e733586eae4","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}