{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:KR457BKDWH2I5D3PVC6XUNWT4I","short_pith_number":"pith:KR457BKD","schema_version":"1.0","canonical_sha256":"5479df8543b1f48e8f6fa8bd7a36d3e23dcdf2e6c11a632dd9695daa668c9004","source":{"kind":"arxiv","id":"2504.20234","version":1},"attestation_state":"computed","paper":{"title":"Improving trajectory continuity in drone-based crowd monitoring using a set of minimal-cost techniques and deep discriminative correlation filters","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Bartosz Ptak, Marek Kraft","submitted_at":"2025-04-28T20:07:42Z","abstract_excerpt":"Drone-based crowd monitoring is the key technology for applications in surveillance, public safety, and event management. However, maintaining tracking continuity and consistency remains a significant challenge. Traditional detection-assignment tracking methods struggle with false positives, false negatives, and frequent identity switches, leading to degraded counting accuracy and making in-depth analysis impossible. This paper introduces a point-oriented online tracking algorithm that improves trajectory continuity and counting reliability in drone-based crowd monitoring. Our method builds on"},"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":"2504.20234","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-28T20:07:42Z","cross_cats_sorted":["cs.RO"],"title_canon_sha256":"7052373137c53212db26f461a3510a2467a86c7fad3f174e7aa82e3ddf6af6e4","abstract_canon_sha256":"71d02a20a28e071a689673fd8717bd9aa685fccf9b1e3fcd7c864e4cd517a8a0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:55:38.906053Z","signature_b64":"WxWgqR5HaSwBcJRgib9TCOwHPbLjyrx87tOyTom+Nb9o4mL6ZD2ClmcrsLkC0WOHS0k38lu800HTjhhgiPTjDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5479df8543b1f48e8f6fa8bd7a36d3e23dcdf2e6c11a632dd9695daa668c9004","last_reissued_at":"2026-07-05T10:55:38.905627Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:55:38.905627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving trajectory continuity in drone-based crowd monitoring using a set of minimal-cost techniques and deep discriminative correlation filters","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.RO"],"primary_cat":"cs.CV","authors_text":"Bartosz Ptak, Marek Kraft","submitted_at":"2025-04-28T20:07:42Z","abstract_excerpt":"Drone-based crowd monitoring is the key technology for applications in surveillance, public safety, and event management. However, maintaining tracking continuity and consistency remains a significant challenge. Traditional detection-assignment tracking methods struggle with false positives, false negatives, and frequent identity switches, leading to degraded counting accuracy and making in-depth analysis impossible. This paper introduces a point-oriented online tracking algorithm that improves trajectory continuity and counting reliability in drone-based crowd monitoring. Our method builds on"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.20234","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.20234/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2504.20234","created_at":"2026-07-05T10:55:38.905682+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.20234v1","created_at":"2026-07-05T10:55:38.905682+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.20234","created_at":"2026-07-05T10:55:38.905682+00:00"},{"alias_kind":"pith_short_12","alias_value":"KR457BKDWH2I","created_at":"2026-07-05T10:55:38.905682+00:00"},{"alias_kind":"pith_short_16","alias_value":"KR457BKDWH2I5D3P","created_at":"2026-07-05T10:55:38.905682+00:00"},{"alias_kind":"pith_short_8","alias_value":"KR457BKD","created_at":"2026-07-05T10:55:38.905682+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/KR457BKDWH2I5D3PVC6XUNWT4I","json":"https://pith.science/pith/KR457BKDWH2I5D3PVC6XUNWT4I.json","graph_json":"https://pith.science/api/pith-number/KR457BKDWH2I5D3PVC6XUNWT4I/graph.json","events_json":"https://pith.science/api/pith-number/KR457BKDWH2I5D3PVC6XUNWT4I/events.json","paper":"https://pith.science/paper/KR457BKD"},"agent_actions":{"view_html":"https://pith.science/pith/KR457BKDWH2I5D3PVC6XUNWT4I","download_json":"https://pith.science/pith/KR457BKDWH2I5D3PVC6XUNWT4I.json","view_paper":"https://pith.science/paper/KR457BKD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.20234&json=true","fetch_graph":"https://pith.science/api/pith-number/KR457BKDWH2I5D3PVC6XUNWT4I/graph.json","fetch_events":"https://pith.science/api/pith-number/KR457BKDWH2I5D3PVC6XUNWT4I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KR457BKDWH2I5D3PVC6XUNWT4I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KR457BKDWH2I5D3PVC6XUNWT4I/action/storage_attestation","attest_author":"https://pith.science/pith/KR457BKDWH2I5D3PVC6XUNWT4I/action/author_attestation","sign_citation":"https://pith.science/pith/KR457BKDWH2I5D3PVC6XUNWT4I/action/citation_signature","submit_replication":"https://pith.science/pith/KR457BKDWH2I5D3PVC6XUNWT4I/action/replication_record"}},"created_at":"2026-07-05T10:55:38.905682+00:00","updated_at":"2026-07-05T10:55:38.905682+00:00"}