{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:OR5QUGX2B4HEYC7H6TINEAKRWR","short_pith_number":"pith:OR5QUGX2","schema_version":"1.0","canonical_sha256":"747b0a1afa0f0e4c0be7f4d0d20151b468b505de87c3ca0cd8095a69cbe17cdc","source":{"kind":"arxiv","id":"1509.09089","version":1},"attestation_state":"computed","paper":{"title":"Moving Object Detection in Video Using Saliency Map and Subspace Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jing Pan, Li Ye, Xuelong Li, Yanwei Pang","submitted_at":"2015-09-30T09:13:20Z","abstract_excerpt":"Moving object detection is a key to intelligent video analysis. On the one hand, what moves is not only interesting objects but also noise and cluttered background. On the other hand, moving objects without rich texture are prone not to be detected. So there are undesirable false alarms and missed alarms in many algorithms of moving object detection. To reduce the false alarms and missed alarms, in this paper, we propose to incorporate a saliency map into an incremental subspace analysis framework where the saliency map makes estimated background has less chance than foreground (i.e., moving o"},"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":"1509.09089","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-09-30T09:13:20Z","cross_cats_sorted":[],"title_canon_sha256":"baca8cadc0820c37cc8389ee409196b0c4a61ae5990700af54541a8a625e1810","abstract_canon_sha256":"33c3206babba3f0bb629d3bdb976e45d2b1eacdf4c8aee918276033e97ecb8c7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:31:29.091592Z","signature_b64":"6ItGHQsahV839O0R+ZD3SpQeSQadq1alfxk9JWv2VSRjp4wIxC+FJ4qem1j3J30843WMri9CKJwHqxEylY33Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"747b0a1afa0f0e4c0be7f4d0d20151b468b505de87c3ca0cd8095a69cbe17cdc","last_reissued_at":"2026-05-18T01:31:29.090916Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:31:29.090916Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Moving Object Detection in Video Using Saliency Map and Subspace Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jing Pan, Li Ye, Xuelong Li, Yanwei Pang","submitted_at":"2015-09-30T09:13:20Z","abstract_excerpt":"Moving object detection is a key to intelligent video analysis. On the one hand, what moves is not only interesting objects but also noise and cluttered background. On the other hand, moving objects without rich texture are prone not to be detected. So there are undesirable false alarms and missed alarms in many algorithms of moving object detection. To reduce the false alarms and missed alarms, in this paper, we propose to incorporate a saliency map into an incremental subspace analysis framework where the saliency map makes estimated background has less chance than foreground (i.e., moving o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1509.09089","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":"1509.09089","created_at":"2026-05-18T01:31:29.090997+00:00"},{"alias_kind":"arxiv_version","alias_value":"1509.09089v1","created_at":"2026-05-18T01:31:29.090997+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1509.09089","created_at":"2026-05-18T01:31:29.090997+00:00"},{"alias_kind":"pith_short_12","alias_value":"OR5QUGX2B4HE","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_16","alias_value":"OR5QUGX2B4HEYC7H","created_at":"2026-05-18T12:29:34.919912+00:00"},{"alias_kind":"pith_short_8","alias_value":"OR5QUGX2","created_at":"2026-05-18T12:29:34.919912+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/OR5QUGX2B4HEYC7H6TINEAKRWR","json":"https://pith.science/pith/OR5QUGX2B4HEYC7H6TINEAKRWR.json","graph_json":"https://pith.science/api/pith-number/OR5QUGX2B4HEYC7H6TINEAKRWR/graph.json","events_json":"https://pith.science/api/pith-number/OR5QUGX2B4HEYC7H6TINEAKRWR/events.json","paper":"https://pith.science/paper/OR5QUGX2"},"agent_actions":{"view_html":"https://pith.science/pith/OR5QUGX2B4HEYC7H6TINEAKRWR","download_json":"https://pith.science/pith/OR5QUGX2B4HEYC7H6TINEAKRWR.json","view_paper":"https://pith.science/paper/OR5QUGX2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1509.09089&json=true","fetch_graph":"https://pith.science/api/pith-number/OR5QUGX2B4HEYC7H6TINEAKRWR/graph.json","fetch_events":"https://pith.science/api/pith-number/OR5QUGX2B4HEYC7H6TINEAKRWR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OR5QUGX2B4HEYC7H6TINEAKRWR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OR5QUGX2B4HEYC7H6TINEAKRWR/action/storage_attestation","attest_author":"https://pith.science/pith/OR5QUGX2B4HEYC7H6TINEAKRWR/action/author_attestation","sign_citation":"https://pith.science/pith/OR5QUGX2B4HEYC7H6TINEAKRWR/action/citation_signature","submit_replication":"https://pith.science/pith/OR5QUGX2B4HEYC7H6TINEAKRWR/action/replication_record"}},"created_at":"2026-05-18T01:31:29.090997+00:00","updated_at":"2026-05-18T01:31:29.090997+00:00"}