{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZVL6QR4UBWCIJRCVPKCRTWG44L","short_pith_number":"pith:ZVL6QR4U","schema_version":"1.0","canonical_sha256":"cd57e847940d8484c4557a8519d8dce2cdd204f1fb32371d6797062bed898517","source":{"kind":"arxiv","id":"1801.02031","version":1},"attestation_state":"computed","paper":{"title":"ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongcheng Wang, Larry S. Davis, Ruichi Yu","submitted_at":"2018-01-06T15:19:06Z","abstract_excerpt":"This paper addresses the problem of detecting relevant motion caused by objects of interest (e.g., person and vehicles) in large scale home surveillance videos. The traditional method usually consists of two separate steps, i.e., detecting moving objects with background subtraction running on the camera, and filtering out nuisance motion events (e.g., trees, cloud, shadow, rain/snow, flag) with deep learning based object detection and tracking running on cloud. The method is extremely slow and therefore not cost effective, and does not fully leverage the spatial-temporal redundancies with a pr"},"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":"1801.02031","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-06T15:19:06Z","cross_cats_sorted":[],"title_canon_sha256":"f4a6616e7ada2418591d453fdb027ae02b0f8faaa2ea913140b17b2aa0536554","abstract_canon_sha256":"eca3f1df097c74c7ba4626fcbded8456449cc5d3ee57daad5532e3630101f2ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:35.107357Z","signature_b64":"3QHi9y5trLN3ml+jmsGhFad9xRuBKjJtRUGqSzbbD6uBIzLgohjjr61cfDwXw62jyMXcy659ecvdCHiMXCFIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd57e847940d8484c4557a8519d8dce2cdd204f1fb32371d6797062bed898517","last_reissued_at":"2026-05-18T00:26:35.106648Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:35.106648Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ReMotENet: Efficient Relevant Motion Event Detection for Large-scale Home Surveillance Videos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongcheng Wang, Larry S. Davis, Ruichi Yu","submitted_at":"2018-01-06T15:19:06Z","abstract_excerpt":"This paper addresses the problem of detecting relevant motion caused by objects of interest (e.g., person and vehicles) in large scale home surveillance videos. The traditional method usually consists of two separate steps, i.e., detecting moving objects with background subtraction running on the camera, and filtering out nuisance motion events (e.g., trees, cloud, shadow, rain/snow, flag) with deep learning based object detection and tracking running on cloud. The method is extremely slow and therefore not cost effective, and does not fully leverage the spatial-temporal redundancies with a pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.02031","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":"1801.02031","created_at":"2026-05-18T00:26:35.106755+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.02031v1","created_at":"2026-05-18T00:26:35.106755+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.02031","created_at":"2026-05-18T00:26:35.106755+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZVL6QR4UBWCI","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZVL6QR4UBWCIJRCV","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZVL6QR4U","created_at":"2026-05-18T12:33:07.085635+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZVL6QR4UBWCIJRCVPKCRTWG44L","json":"https://pith.science/pith/ZVL6QR4UBWCIJRCVPKCRTWG44L.json","graph_json":"https://pith.science/api/pith-number/ZVL6QR4UBWCIJRCVPKCRTWG44L/graph.json","events_json":"https://pith.science/api/pith-number/ZVL6QR4UBWCIJRCVPKCRTWG44L/events.json","paper":"https://pith.science/paper/ZVL6QR4U"},"agent_actions":{"view_html":"https://pith.science/pith/ZVL6QR4UBWCIJRCVPKCRTWG44L","download_json":"https://pith.science/pith/ZVL6QR4UBWCIJRCVPKCRTWG44L.json","view_paper":"https://pith.science/paper/ZVL6QR4U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.02031&json=true","fetch_graph":"https://pith.science/api/pith-number/ZVL6QR4UBWCIJRCVPKCRTWG44L/graph.json","fetch_events":"https://pith.science/api/pith-number/ZVL6QR4UBWCIJRCVPKCRTWG44L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZVL6QR4UBWCIJRCVPKCRTWG44L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZVL6QR4UBWCIJRCVPKCRTWG44L/action/storage_attestation","attest_author":"https://pith.science/pith/ZVL6QR4UBWCIJRCVPKCRTWG44L/action/author_attestation","sign_citation":"https://pith.science/pith/ZVL6QR4UBWCIJRCVPKCRTWG44L/action/citation_signature","submit_replication":"https://pith.science/pith/ZVL6QR4UBWCIJRCVPKCRTWG44L/action/replication_record"}},"created_at":"2026-05-18T00:26:35.106755+00:00","updated_at":"2026-05-18T00:26:35.106755+00:00"}