{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:MWRAU4AOXXUPOO2MH4ARKUH5UQ","short_pith_number":"pith:MWRAU4AO","schema_version":"1.0","canonical_sha256":"65a20a700ebde8f73b4c3f011550fda40f1b62d8cad265214b73e82d2acabf46","source":{"kind":"arxiv","id":"1107.5850","version":2},"attestation_state":"computed","paper":{"title":"Confidence-Based Dynamic Classifier Combination For Mean-Shift Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hakan Erdogan, Ibrahim Saygin Topkaya","submitted_at":"2011-07-29T01:08:52Z","abstract_excerpt":"We introduce a novel tracking technique which uses dynamic confidence-based fusion of two different information sources for robust and efficient tracking of visual objects. Mean-shift tracking is a popular and well known method used in object tracking problems. Originally, the algorithm uses a similarity measure which is optimized by shifting a search area to the center of a generated weight image to track objects. Recent improvements on the original mean-shift algorithm involves using a classifier that differentiates the object from its surroundings. We adopt this classifier-based approach an"},"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":"1107.5850","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2011-07-29T01:08:52Z","cross_cats_sorted":[],"title_canon_sha256":"b2c4cd90962901179c88c8a4978dd6a2b01d3c63aff340577f7d528e27b8da7c","abstract_canon_sha256":"04340d500cb4208cf33f7052d1e2f3263c5d85c5bc8ebc9b5ce2e956bb894763"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:21:42.576146Z","signature_b64":"AsCYNVmbDJGFuOObDxF6C97FkAJbhiOPOFkzW0Kc1rgH83wG7AIGVh5D4WwfWT32gFTL3QUe+mqV996uEuebBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"65a20a700ebde8f73b4c3f011550fda40f1b62d8cad265214b73e82d2acabf46","last_reissued_at":"2026-05-18T02:21:42.575722Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:21:42.575722Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Confidence-Based Dynamic Classifier Combination For Mean-Shift Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hakan Erdogan, Ibrahim Saygin Topkaya","submitted_at":"2011-07-29T01:08:52Z","abstract_excerpt":"We introduce a novel tracking technique which uses dynamic confidence-based fusion of two different information sources for robust and efficient tracking of visual objects. Mean-shift tracking is a popular and well known method used in object tracking problems. Originally, the algorithm uses a similarity measure which is optimized by shifting a search area to the center of a generated weight image to track objects. Recent improvements on the original mean-shift algorithm involves using a classifier that differentiates the object from its surroundings. We adopt this classifier-based approach an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1107.5850","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":"1107.5850","created_at":"2026-05-18T02:21:42.575783+00:00"},{"alias_kind":"arxiv_version","alias_value":"1107.5850v2","created_at":"2026-05-18T02:21:42.575783+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1107.5850","created_at":"2026-05-18T02:21:42.575783+00:00"},{"alias_kind":"pith_short_12","alias_value":"MWRAU4AOXXUP","created_at":"2026-05-18T12:26:37.096874+00:00"},{"alias_kind":"pith_short_16","alias_value":"MWRAU4AOXXUPOO2M","created_at":"2026-05-18T12:26:37.096874+00:00"},{"alias_kind":"pith_short_8","alias_value":"MWRAU4AO","created_at":"2026-05-18T12:26:37.096874+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/MWRAU4AOXXUPOO2MH4ARKUH5UQ","json":"https://pith.science/pith/MWRAU4AOXXUPOO2MH4ARKUH5UQ.json","graph_json":"https://pith.science/api/pith-number/MWRAU4AOXXUPOO2MH4ARKUH5UQ/graph.json","events_json":"https://pith.science/api/pith-number/MWRAU4AOXXUPOO2MH4ARKUH5UQ/events.json","paper":"https://pith.science/paper/MWRAU4AO"},"agent_actions":{"view_html":"https://pith.science/pith/MWRAU4AOXXUPOO2MH4ARKUH5UQ","download_json":"https://pith.science/pith/MWRAU4AOXXUPOO2MH4ARKUH5UQ.json","view_paper":"https://pith.science/paper/MWRAU4AO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1107.5850&json=true","fetch_graph":"https://pith.science/api/pith-number/MWRAU4AOXXUPOO2MH4ARKUH5UQ/graph.json","fetch_events":"https://pith.science/api/pith-number/MWRAU4AOXXUPOO2MH4ARKUH5UQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MWRAU4AOXXUPOO2MH4ARKUH5UQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MWRAU4AOXXUPOO2MH4ARKUH5UQ/action/storage_attestation","attest_author":"https://pith.science/pith/MWRAU4AOXXUPOO2MH4ARKUH5UQ/action/author_attestation","sign_citation":"https://pith.science/pith/MWRAU4AOXXUPOO2MH4ARKUH5UQ/action/citation_signature","submit_replication":"https://pith.science/pith/MWRAU4AOXXUPOO2MH4ARKUH5UQ/action/replication_record"}},"created_at":"2026-05-18T02:21:42.575783+00:00","updated_at":"2026-05-18T02:21:42.575783+00:00"}