{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:PJ2KWWQKPBKYB5TBW4VWYFLDIG","short_pith_number":"pith:PJ2KWWQK","schema_version":"1.0","canonical_sha256":"7a74ab5a0a785580f661b72b6c156341a66c019843965eacb846b1e9fa61b3a2","source":{"kind":"arxiv","id":"1908.03945","version":6},"attestation_state":"computed","paper":{"title":"Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nathanael L. Baisa","submitted_at":"2019-08-11T18:15:57Z","abstract_excerpt":"We propose a novel online multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches like MHT and point-process-based approaches like PHD filter, and it has linear complexity while maintaining track identities. We apply this filter for tracking multiple targets in video sequences acquired under varying environmental conditions and targets density using a tracking-by-detection approach. We also adopt deep CNN appearance representation by training a verificati"},"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":"1908.03945","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-08-11T18:15:57Z","cross_cats_sorted":[],"title_canon_sha256":"a3a3ea7d4ca8e5c1ec49d790d88be4c663066ebc4e75ad1778d5689854069a88","abstract_canon_sha256":"254bc31a8fb5c0b4facdca4159f8b6309a400722f641354662b9db3c8b1a76ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:41:48.811853Z","signature_b64":"/xPnV+m8PKgwsgCdSD6t6WUQWHxSXqcPE1X2624ElALVg+BUdCAVJ98QWq6rA4ZS/iuOxCWoROMv6X1YeVJ1BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a74ab5a0a785580f661b72b6c156341a66c019843965eacb846b1e9fa61b3a2","last_reissued_at":"2026-07-05T01:41:48.811300Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:41:48.811300Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Online Multi-target Visual Tracking using a HISP Filter with Discriminative Deep Appearance Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nathanael L. Baisa","submitted_at":"2019-08-11T18:15:57Z","abstract_excerpt":"We propose a novel online multi-target visual tracker based on the recently developed Hypothesized and Independent Stochastic Population (HISP) filter. The HISP filter combines advantages of traditional tracking approaches like MHT and point-process-based approaches like PHD filter, and it has linear complexity while maintaining track identities. We apply this filter for tracking multiple targets in video sequences acquired under varying environmental conditions and targets density using a tracking-by-detection approach. We also adopt deep CNN appearance representation by training a verificati"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.03945","kind":"arxiv","version":6},"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/1908.03945/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":"1908.03945","created_at":"2026-07-05T01:41:48.811365+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.03945v6","created_at":"2026-07-05T01:41:48.811365+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.03945","created_at":"2026-07-05T01:41:48.811365+00:00"},{"alias_kind":"pith_short_12","alias_value":"PJ2KWWQKPBKY","created_at":"2026-07-05T01:41:48.811365+00:00"},{"alias_kind":"pith_short_16","alias_value":"PJ2KWWQKPBKYB5TB","created_at":"2026-07-05T01:41:48.811365+00:00"},{"alias_kind":"pith_short_8","alias_value":"PJ2KWWQK","created_at":"2026-07-05T01:41:48.811365+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/PJ2KWWQKPBKYB5TBW4VWYFLDIG","json":"https://pith.science/pith/PJ2KWWQKPBKYB5TBW4VWYFLDIG.json","graph_json":"https://pith.science/api/pith-number/PJ2KWWQKPBKYB5TBW4VWYFLDIG/graph.json","events_json":"https://pith.science/api/pith-number/PJ2KWWQKPBKYB5TBW4VWYFLDIG/events.json","paper":"https://pith.science/paper/PJ2KWWQK"},"agent_actions":{"view_html":"https://pith.science/pith/PJ2KWWQKPBKYB5TBW4VWYFLDIG","download_json":"https://pith.science/pith/PJ2KWWQKPBKYB5TBW4VWYFLDIG.json","view_paper":"https://pith.science/paper/PJ2KWWQK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.03945&json=true","fetch_graph":"https://pith.science/api/pith-number/PJ2KWWQKPBKYB5TBW4VWYFLDIG/graph.json","fetch_events":"https://pith.science/api/pith-number/PJ2KWWQKPBKYB5TBW4VWYFLDIG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PJ2KWWQKPBKYB5TBW4VWYFLDIG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PJ2KWWQKPBKYB5TBW4VWYFLDIG/action/storage_attestation","attest_author":"https://pith.science/pith/PJ2KWWQKPBKYB5TBW4VWYFLDIG/action/author_attestation","sign_citation":"https://pith.science/pith/PJ2KWWQKPBKYB5TBW4VWYFLDIG/action/citation_signature","submit_replication":"https://pith.science/pith/PJ2KWWQKPBKYB5TBW4VWYFLDIG/action/replication_record"}},"created_at":"2026-07-05T01:41:48.811365+00:00","updated_at":"2026-07-05T01:41:48.811365+00:00"}