{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:PEHO6OEFKFD6C7C6GHDWJOQ7XK","short_pith_number":"pith:PEHO6OEF","schema_version":"1.0","canonical_sha256":"790eef38855147e17c5e31c764ba1fbaa542b973a8f5d95e9c739f69fecfa1cb","source":{"kind":"arxiv","id":"1404.7592","version":1},"attestation_state":"computed","paper":{"title":"Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jacob Grosek, J. Nathan Kutz","submitted_at":"2014-04-30T05:21:27Z","abstract_excerpt":"This paper introduces the method of dynamic mode decomposition (DMD) for robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. The method is a novel application of a technique used for characterizing nonlinear dynamical systems in an equation-free manner by decomposing the state of the system into low-rank terms whose Fourier components in time are known. DMD terms with Fourier frequencies near the origin (zero-modes) are interpreted as background (low-rank) portions of the given video frames, and the terms with Fourier frequencies bounded"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1404.7592","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-04-30T05:21:27Z","cross_cats_sorted":[],"title_canon_sha256":"4fef8f55728676c8d13ace84991527b8f210191469d06a4a481f7141f15577c8","abstract_canon_sha256":"185afd90c1799d39675e794388796d0499274e2d66da24aa98c393788237fd70"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:52:55.260633Z","signature_b64":"op7kLoQzD6U567TgmD2Ymw5M9UK2phRzsCZ0M96pBQqJjp3Aw6VTPS2JUEEhQPYSdENySLjJM7/Al9YU3A29AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"790eef38855147e17c5e31c764ba1fbaa542b973a8f5d95e9c739f69fecfa1cb","last_reissued_at":"2026-05-18T02:52:55.260099Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:52:55.260099Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Mode Decomposition for Real-Time Background/Foreground Separation in Video","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jacob Grosek, J. Nathan Kutz","submitted_at":"2014-04-30T05:21:27Z","abstract_excerpt":"This paper introduces the method of dynamic mode decomposition (DMD) for robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. The method is a novel application of a technique used for characterizing nonlinear dynamical systems in an equation-free manner by decomposing the state of the system into low-rank terms whose Fourier components in time are known. DMD terms with Fourier frequencies near the origin (zero-modes) are interpreted as background (low-rank) portions of the given video frames, and the terms with Fourier frequencies bounded"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1404.7592","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1404.7592","created_at":"2026-05-18T02:52:55.260171+00:00"},{"alias_kind":"arxiv_version","alias_value":"1404.7592v1","created_at":"2026-05-18T02:52:55.260171+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1404.7592","created_at":"2026-05-18T02:52:55.260171+00:00"},{"alias_kind":"pith_short_12","alias_value":"PEHO6OEFKFD6","created_at":"2026-05-18T12:28:43.426989+00:00"},{"alias_kind":"pith_short_16","alias_value":"PEHO6OEFKFD6C7C6","created_at":"2026-05-18T12:28:43.426989+00:00"},{"alias_kind":"pith_short_8","alias_value":"PEHO6OEF","created_at":"2026-05-18T12:28:43.426989+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.16240","citing_title":"CollideNet: Hierarchical Multi-scale Video Representation Learning with Disentanglement for Time-To-Collision Forecasting","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PEHO6OEFKFD6C7C6GHDWJOQ7XK","json":"https://pith.science/pith/PEHO6OEFKFD6C7C6GHDWJOQ7XK.json","graph_json":"https://pith.science/api/pith-number/PEHO6OEFKFD6C7C6GHDWJOQ7XK/graph.json","events_json":"https://pith.science/api/pith-number/PEHO6OEFKFD6C7C6GHDWJOQ7XK/events.json","paper":"https://pith.science/paper/PEHO6OEF"},"agent_actions":{"view_html":"https://pith.science/pith/PEHO6OEFKFD6C7C6GHDWJOQ7XK","download_json":"https://pith.science/pith/PEHO6OEFKFD6C7C6GHDWJOQ7XK.json","view_paper":"https://pith.science/paper/PEHO6OEF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1404.7592&json=true","fetch_graph":"https://pith.science/api/pith-number/PEHO6OEFKFD6C7C6GHDWJOQ7XK/graph.json","fetch_events":"https://pith.science/api/pith-number/PEHO6OEFKFD6C7C6GHDWJOQ7XK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PEHO6OEFKFD6C7C6GHDWJOQ7XK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PEHO6OEFKFD6C7C6GHDWJOQ7XK/action/storage_attestation","attest_author":"https://pith.science/pith/PEHO6OEFKFD6C7C6GHDWJOQ7XK/action/author_attestation","sign_citation":"https://pith.science/pith/PEHO6OEFKFD6C7C6GHDWJOQ7XK/action/citation_signature","submit_replication":"https://pith.science/pith/PEHO6OEFKFD6C7C6GHDWJOQ7XK/action/replication_record"}},"created_at":"2026-05-18T02:52:55.260171+00:00","updated_at":"2026-05-18T02:52:55.260171+00:00"}