{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:W7RVOIWRAH2I2UM2UAM7PMJQX7","short_pith_number":"pith:W7RVOIWR","schema_version":"1.0","canonical_sha256":"b7e35722d101f48d519aa019f7b130bfd87cfe0c99dd8395a66eb1e1083f56e3","source":{"kind":"arxiv","id":"1811.07498","version":1},"attestation_state":"computed","paper":{"title":"Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dacheng Tao, Jiamiao Xu, Peng Zhang, Shujian Yu, Xiao-Yuan Jing, Xinge You, Xiubao Jiang","submitted_at":"2018-11-19T04:44:00Z","abstract_excerpt":"It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this work, in which the temporal relatedness is reconciled under a multi-task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate the resu"},"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":"1811.07498","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-11-19T04:44:00Z","cross_cats_sorted":[],"title_canon_sha256":"6d42104b6d79b895e104e5a1aa56ff79b5756fbea5d130c507c8da87cb92761a","abstract_canon_sha256":"e72741f5c346eb7564e2f6440ee34bd4c611b67d6141e0672a11170d0b2a21c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:07.751252Z","signature_b64":"ksG13DKnOipnLX3vmKwHLRP0KxTVRunKae3s9/i8ew5iiR70S665NQvT/Q3GwztQ17WXnuNzdTIGwrNAhVgyCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7e35722d101f48d519aa019f7b130bfd87cfe0c99dd8395a66eb1e1083f56e3","last_reissued_at":"2026-05-18T00:00:07.750546Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:07.750546Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dacheng Tao, Jiamiao Xu, Peng Zhang, Shujian Yu, Xiao-Yuan Jing, Xinge You, Xiubao Jiang","submitted_at":"2018-11-19T04:44:00Z","abstract_excerpt":"It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model. Motivated by this idea, we propose a novel correlation filter-based tracker in this work, in which the temporal relatedness is reconciled under a multi-task learning framework and the multiple feature cues are modeled using a multi-view learning approach. We demonstrate the resu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1811.07498","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":""},"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":"1811.07498","created_at":"2026-05-18T00:00:07.750645+00:00"},{"alias_kind":"arxiv_version","alias_value":"1811.07498v1","created_at":"2026-05-18T00:00:07.750645+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1811.07498","created_at":"2026-05-18T00:00:07.750645+00:00"},{"alias_kind":"pith_short_12","alias_value":"W7RVOIWRAH2I","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_16","alias_value":"W7RVOIWRAH2I2UM2","created_at":"2026-05-18T12:32:59.047623+00:00"},{"alias_kind":"pith_short_8","alias_value":"W7RVOIWR","created_at":"2026-05-18T12:32:59.047623+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/W7RVOIWRAH2I2UM2UAM7PMJQX7","json":"https://pith.science/pith/W7RVOIWRAH2I2UM2UAM7PMJQX7.json","graph_json":"https://pith.science/api/pith-number/W7RVOIWRAH2I2UM2UAM7PMJQX7/graph.json","events_json":"https://pith.science/api/pith-number/W7RVOIWRAH2I2UM2UAM7PMJQX7/events.json","paper":"https://pith.science/paper/W7RVOIWR"},"agent_actions":{"view_html":"https://pith.science/pith/W7RVOIWRAH2I2UM2UAM7PMJQX7","download_json":"https://pith.science/pith/W7RVOIWRAH2I2UM2UAM7PMJQX7.json","view_paper":"https://pith.science/paper/W7RVOIWR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1811.07498&json=true","fetch_graph":"https://pith.science/api/pith-number/W7RVOIWRAH2I2UM2UAM7PMJQX7/graph.json","fetch_events":"https://pith.science/api/pith-number/W7RVOIWRAH2I2UM2UAM7PMJQX7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W7RVOIWRAH2I2UM2UAM7PMJQX7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W7RVOIWRAH2I2UM2UAM7PMJQX7/action/storage_attestation","attest_author":"https://pith.science/pith/W7RVOIWRAH2I2UM2UAM7PMJQX7/action/author_attestation","sign_citation":"https://pith.science/pith/W7RVOIWRAH2I2UM2UAM7PMJQX7/action/citation_signature","submit_replication":"https://pith.science/pith/W7RVOIWRAH2I2UM2UAM7PMJQX7/action/replication_record"}},"created_at":"2026-05-18T00:00:07.750645+00:00","updated_at":"2026-05-18T00:00:07.750645+00:00"}