{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:PCR4PIIZTMXRZBWCSPYS7XFKWQ","short_pith_number":"pith:PCR4PIIZ","schema_version":"1.0","canonical_sha256":"78a3c7a1199b2f1c86c293f12fdcaab417ff6392b110ccca54807f5b6db367a4","source":{"kind":"arxiv","id":"2204.11410","version":1},"attestation_state":"computed","paper":{"title":"Single Object Tracking Research: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Qing Guo, Qinghua Hu, Ruize Han, Wei Feng","submitted_at":"2022-04-25T02:59:15Z","abstract_excerpt":"Visual object tracking is an important task in computer vision, which has many real-world applications, e.g., video surveillance, visual navigation. Visual object tracking also has many challenges, e.g., object occlusion and deformation. To solve above problems and track the target accurately and efficiently, many tracking algorithms have emerged in recent years. This paper presents the rationale and representative works of two most popular tracking frameworks in past ten years, i.e., the corelation filter and Siamese network for object tracking. Then we present some deep learning based tracki"},"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":"2204.11410","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-04-25T02:59:15Z","cross_cats_sorted":[],"title_canon_sha256":"1c53f64ac656db4656bd9d72e898fe57ab7346af35ce49bfce55b8e1b3457f93","abstract_canon_sha256":"075877e70bbe992e4cace5f7f6449bbcc52af03f480499d2e9c6e459080ad8d0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:17:27.467099Z","signature_b64":"8rsPOfGHkSFHKgCi0Qx4fme6pjvvimc5ungJlT21YIX2ec3hkI4b9Ax3RUP4mfNwrrEhI/7alxDhd/rJ8Z8+Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"78a3c7a1199b2f1c86c293f12fdcaab417ff6392b110ccca54807f5b6db367a4","last_reissued_at":"2026-07-05T04:17:27.466610Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:17:27.466610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Single Object Tracking Research: A Survey","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Qing Guo, Qinghua Hu, Ruize Han, Wei Feng","submitted_at":"2022-04-25T02:59:15Z","abstract_excerpt":"Visual object tracking is an important task in computer vision, which has many real-world applications, e.g., video surveillance, visual navigation. Visual object tracking also has many challenges, e.g., object occlusion and deformation. To solve above problems and track the target accurately and efficiently, many tracking algorithms have emerged in recent years. This paper presents the rationale and representative works of two most popular tracking frameworks in past ten years, i.e., the corelation filter and Siamese network for object tracking. Then we present some deep learning based tracki"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.11410","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/2204.11410/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":"2204.11410","created_at":"2026-07-05T04:17:27.466664+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.11410v1","created_at":"2026-07-05T04:17:27.466664+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.11410","created_at":"2026-07-05T04:17:27.466664+00:00"},{"alias_kind":"pith_short_12","alias_value":"PCR4PIIZTMXR","created_at":"2026-07-05T04:17:27.466664+00:00"},{"alias_kind":"pith_short_16","alias_value":"PCR4PIIZTMXRZBWC","created_at":"2026-07-05T04:17:27.466664+00:00"},{"alias_kind":"pith_short_8","alias_value":"PCR4PIIZ","created_at":"2026-07-05T04:17:27.466664+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/PCR4PIIZTMXRZBWCSPYS7XFKWQ","json":"https://pith.science/pith/PCR4PIIZTMXRZBWCSPYS7XFKWQ.json","graph_json":"https://pith.science/api/pith-number/PCR4PIIZTMXRZBWCSPYS7XFKWQ/graph.json","events_json":"https://pith.science/api/pith-number/PCR4PIIZTMXRZBWCSPYS7XFKWQ/events.json","paper":"https://pith.science/paper/PCR4PIIZ"},"agent_actions":{"view_html":"https://pith.science/pith/PCR4PIIZTMXRZBWCSPYS7XFKWQ","download_json":"https://pith.science/pith/PCR4PIIZTMXRZBWCSPYS7XFKWQ.json","view_paper":"https://pith.science/paper/PCR4PIIZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.11410&json=true","fetch_graph":"https://pith.science/api/pith-number/PCR4PIIZTMXRZBWCSPYS7XFKWQ/graph.json","fetch_events":"https://pith.science/api/pith-number/PCR4PIIZTMXRZBWCSPYS7XFKWQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PCR4PIIZTMXRZBWCSPYS7XFKWQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PCR4PIIZTMXRZBWCSPYS7XFKWQ/action/storage_attestation","attest_author":"https://pith.science/pith/PCR4PIIZTMXRZBWCSPYS7XFKWQ/action/author_attestation","sign_citation":"https://pith.science/pith/PCR4PIIZTMXRZBWCSPYS7XFKWQ/action/citation_signature","submit_replication":"https://pith.science/pith/PCR4PIIZTMXRZBWCSPYS7XFKWQ/action/replication_record"}},"created_at":"2026-07-05T04:17:27.466664+00:00","updated_at":"2026-07-05T04:17:27.466664+00:00"}