{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ISFVUMEKKHH3S3QXCAVIXQIVFJ","short_pith_number":"pith:ISFVUMEK","schema_version":"1.0","canonical_sha256":"448b5a308a51cfb96e17102a8bc1152a6fbc605b5d6a5a0a7218252e7915121e","source":{"kind":"arxiv","id":"1805.07903","version":1},"attestation_state":"computed","paper":{"title":"Unsupervised Deep Context Prediction for Background Foreground Separation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arif Mahmood, Maryam Sultana, Sajid Javed, Soon Ki Jung","submitted_at":"2018-05-21T06:12:15Z","abstract_excerpt":"In many advanced video based applications background modeling is a pre-processing step to eliminate redundant data, for instance in tracking or video surveillance applications. Over the past years background subtraction is usually based on low level or hand-crafted features such as raw color components, gradients, or local binary patterns. The background subtraction algorithms performance suffer in the presence of various challenges such as dynamic backgrounds, photometric variations, camera jitters, and shadows. To handle these challenges for the purpose of accurate background modeling we pro"},"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":"1805.07903","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2018-05-21T06:12:15Z","cross_cats_sorted":[],"title_canon_sha256":"a1e26468eabf0b50620bb5a563c27c13bdd08d8a568533d34f0d35cfc3de3773","abstract_canon_sha256":"32eb86099acd0bcbc7938e983c2d516ddee9d9486341b6cd6e6465f61c976339"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:37.453826Z","signature_b64":"TnsfVYcO7BDHV0mRsyGUNQDNiq/4A8sw3hLIsS+n7XqJuHS0zkvC2Xc/aA9IopS9IUWgARZahrXo4RG20PPEAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"448b5a308a51cfb96e17102a8bc1152a6fbc605b5d6a5a0a7218252e7915121e","last_reissued_at":"2026-05-18T00:10:37.453189Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:37.453189Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unsupervised Deep Context Prediction for Background Foreground Separation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Arif Mahmood, Maryam Sultana, Sajid Javed, Soon Ki Jung","submitted_at":"2018-05-21T06:12:15Z","abstract_excerpt":"In many advanced video based applications background modeling is a pre-processing step to eliminate redundant data, for instance in tracking or video surveillance applications. Over the past years background subtraction is usually based on low level or hand-crafted features such as raw color components, gradients, or local binary patterns. The background subtraction algorithms performance suffer in the presence of various challenges such as dynamic backgrounds, photometric variations, camera jitters, and shadows. To handle these challenges for the purpose of accurate background modeling we pro"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.07903","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":"1805.07903","created_at":"2026-05-18T00:10:37.453288+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.07903v1","created_at":"2026-05-18T00:10:37.453288+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.07903","created_at":"2026-05-18T00:10:37.453288+00:00"},{"alias_kind":"pith_short_12","alias_value":"ISFVUMEKKHH3","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"ISFVUMEKKHH3S3QX","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"ISFVUMEK","created_at":"2026-05-18T12:32:31.084164+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/ISFVUMEKKHH3S3QXCAVIXQIVFJ","json":"https://pith.science/pith/ISFVUMEKKHH3S3QXCAVIXQIVFJ.json","graph_json":"https://pith.science/api/pith-number/ISFVUMEKKHH3S3QXCAVIXQIVFJ/graph.json","events_json":"https://pith.science/api/pith-number/ISFVUMEKKHH3S3QXCAVIXQIVFJ/events.json","paper":"https://pith.science/paper/ISFVUMEK"},"agent_actions":{"view_html":"https://pith.science/pith/ISFVUMEKKHH3S3QXCAVIXQIVFJ","download_json":"https://pith.science/pith/ISFVUMEKKHH3S3QXCAVIXQIVFJ.json","view_paper":"https://pith.science/paper/ISFVUMEK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.07903&json=true","fetch_graph":"https://pith.science/api/pith-number/ISFVUMEKKHH3S3QXCAVIXQIVFJ/graph.json","fetch_events":"https://pith.science/api/pith-number/ISFVUMEKKHH3S3QXCAVIXQIVFJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ISFVUMEKKHH3S3QXCAVIXQIVFJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ISFVUMEKKHH3S3QXCAVIXQIVFJ/action/storage_attestation","attest_author":"https://pith.science/pith/ISFVUMEKKHH3S3QXCAVIXQIVFJ/action/author_attestation","sign_citation":"https://pith.science/pith/ISFVUMEKKHH3S3QXCAVIXQIVFJ/action/citation_signature","submit_replication":"https://pith.science/pith/ISFVUMEKKHH3S3QXCAVIXQIVFJ/action/replication_record"}},"created_at":"2026-05-18T00:10:37.453288+00:00","updated_at":"2026-05-18T00:10:37.453288+00:00"}