{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:QI44OJUZTTUAXPS53T2BGZNXPO","short_pith_number":"pith:QI44OJUZ","schema_version":"1.0","canonical_sha256":"8239c726999ce80bbe5ddcf41365b77bb3ba20cc41e987d6b76c71c4aa4b2b16","source":{"kind":"arxiv","id":"1612.00220","version":2},"attestation_state":"computed","paper":{"title":"Fully Convolutional Crowd Counting On Highly Congested Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kevin McGuinness, Mark Marsden, Noel E. O'Connor, Suzanne Little","submitted_at":"2016-12-01T12:24:35Z","abstract_excerpt":"In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Zhang et al., 2016). Producing an accurate and robust crowd count estimator using computer vision techniques has attracted significant research interest in recent years. Applications for crowd counting systems exist in many diverse areas including city planning, retail, and of course general public safety. Developing a highly generalised counting model that can be deployed in any surveillance scenario with any camera "},"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":"1612.00220","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-01T12:24:35Z","cross_cats_sorted":[],"title_canon_sha256":"9e6a547f0baa48dfab149c506bf99d70c5559916b16c5285851685f5ec2fe6d0","abstract_canon_sha256":"fe1b0e82610631dae73e56dbfe95754ba38cd9c83fedbcc41eb0e4d5c2e75cea"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:52:43.983886Z","signature_b64":"DW84y77OelMd1X7UMbdD+J3wFpU06Kvo1/RwAXoaENqLsigbr9ezctzlqmiEaGo0qyvmzDk3bkIy+mSzCMwHAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8239c726999ce80bbe5ddcf41365b77bb3ba20cc41e987d6b76c71c4aa4b2b16","last_reissued_at":"2026-05-18T00:52:43.983088Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:52:43.983088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fully Convolutional Crowd Counting On Highly Congested Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Kevin McGuinness, Mark Marsden, Noel E. O'Connor, Suzanne Little","submitted_at":"2016-12-01T12:24:35Z","abstract_excerpt":"In this paper we advance the state-of-the-art for crowd counting in high density scenes by further exploring the idea of a fully convolutional crowd counting model introduced by (Zhang et al., 2016). Producing an accurate and robust crowd count estimator using computer vision techniques has attracted significant research interest in recent years. Applications for crowd counting systems exist in many diverse areas including city planning, retail, and of course general public safety. Developing a highly generalised counting model that can be deployed in any surveillance scenario with any camera "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.00220","kind":"arxiv","version":2},"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":"1612.00220","created_at":"2026-05-18T00:52:43.983238+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.00220v2","created_at":"2026-05-18T00:52:43.983238+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.00220","created_at":"2026-05-18T00:52:43.983238+00:00"},{"alias_kind":"pith_short_12","alias_value":"QI44OJUZTTUA","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_16","alias_value":"QI44OJUZTTUAXPS5","created_at":"2026-05-18T12:30:39.010887+00:00"},{"alias_kind":"pith_short_8","alias_value":"QI44OJUZ","created_at":"2026-05-18T12:30:39.010887+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1908.09066","citing_title":"Robust Regression via Deep Negative Correlation Learning","ref_index":93,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QI44OJUZTTUAXPS53T2BGZNXPO","json":"https://pith.science/pith/QI44OJUZTTUAXPS53T2BGZNXPO.json","graph_json":"https://pith.science/api/pith-number/QI44OJUZTTUAXPS53T2BGZNXPO/graph.json","events_json":"https://pith.science/api/pith-number/QI44OJUZTTUAXPS53T2BGZNXPO/events.json","paper":"https://pith.science/paper/QI44OJUZ"},"agent_actions":{"view_html":"https://pith.science/pith/QI44OJUZTTUAXPS53T2BGZNXPO","download_json":"https://pith.science/pith/QI44OJUZTTUAXPS53T2BGZNXPO.json","view_paper":"https://pith.science/paper/QI44OJUZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.00220&json=true","fetch_graph":"https://pith.science/api/pith-number/QI44OJUZTTUAXPS53T2BGZNXPO/graph.json","fetch_events":"https://pith.science/api/pith-number/QI44OJUZTTUAXPS53T2BGZNXPO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QI44OJUZTTUAXPS53T2BGZNXPO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QI44OJUZTTUAXPS53T2BGZNXPO/action/storage_attestation","attest_author":"https://pith.science/pith/QI44OJUZTTUAXPS53T2BGZNXPO/action/author_attestation","sign_citation":"https://pith.science/pith/QI44OJUZTTUAXPS53T2BGZNXPO/action/citation_signature","submit_replication":"https://pith.science/pith/QI44OJUZTTUAXPS53T2BGZNXPO/action/replication_record"}},"created_at":"2026-05-18T00:52:43.983238+00:00","updated_at":"2026-05-18T00:52:43.983238+00:00"}