{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:JQWMYM3DLIYGHESWDK4MDS36HI","short_pith_number":"pith:JQWMYM3D","schema_version":"1.0","canonical_sha256":"4c2ccc33635a306392561ab8c1cb7e3a08a9d2e1ea4a7eb8137f601fe6d3c39d","source":{"kind":"arxiv","id":"1804.06680","version":1},"attestation_state":"computed","paper":{"title":"Temporal Unknown Incremental Clustering (TUIC) Model for Analysis of Traffic Surveillance Videos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Debi Prosad Dogra, Partha Pratim Roy, Santhosh Kelathodi Kumaran","submitted_at":"2018-04-18T12:29:42Z","abstract_excerpt":"Optimized scene representation is an important characteristic of a framework for detecting abnormalities on live videos. One of the challenges for detecting abnormalities in live videos is real-time detection of objects in a non-parametric way. Another challenge is to efficiently represent the state of objects temporally across frames. In this paper, a Gibbs sampling based heuristic model referred to as Temporal Unknown Incremental Clustering (TUIC) has been proposed to cluster pixels with motion. Pixel motion is first detected using optical flow and a Bayesian algorithm has been applied to as"},"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":"1804.06680","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-04-18T12:29:42Z","cross_cats_sorted":[],"title_canon_sha256":"227d73ad5f8192c1fdfe489394dd1aa97a6865a879f316853308e3ec13d5a494","abstract_canon_sha256":"44ecc0564a3b02a36ef36138525e297a347e9e7d429ec3998884a6b9afa1345e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:44.405195Z","signature_b64":"Nix/Xu1+4GCfF3QQgE529g+++CnrnX8Ei/Gc3FHU3fIK2HkgqD/zjmbA0b34q+pcS7uHgiynAsk5rdQSaeV4Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4c2ccc33635a306392561ab8c1cb7e3a08a9d2e1ea4a7eb8137f601fe6d3c39d","last_reissued_at":"2026-05-18T00:13:44.404744Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:44.404744Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Temporal Unknown Incremental Clustering (TUIC) Model for Analysis of Traffic Surveillance Videos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Debi Prosad Dogra, Partha Pratim Roy, Santhosh Kelathodi Kumaran","submitted_at":"2018-04-18T12:29:42Z","abstract_excerpt":"Optimized scene representation is an important characteristic of a framework for detecting abnormalities on live videos. One of the challenges for detecting abnormalities in live videos is real-time detection of objects in a non-parametric way. Another challenge is to efficiently represent the state of objects temporally across frames. In this paper, a Gibbs sampling based heuristic model referred to as Temporal Unknown Incremental Clustering (TUIC) has been proposed to cluster pixels with motion. Pixel motion is first detected using optical flow and a Bayesian algorithm has been applied to as"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.06680","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":"1804.06680","created_at":"2026-05-18T00:13:44.404807+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.06680v1","created_at":"2026-05-18T00:13:44.404807+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.06680","created_at":"2026-05-18T00:13:44.404807+00:00"},{"alias_kind":"pith_short_12","alias_value":"JQWMYM3DLIYG","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"JQWMYM3DLIYGHESW","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"JQWMYM3D","created_at":"2026-05-18T12:32:31.084164+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/JQWMYM3DLIYGHESWDK4MDS36HI","json":"https://pith.science/pith/JQWMYM3DLIYGHESWDK4MDS36HI.json","graph_json":"https://pith.science/api/pith-number/JQWMYM3DLIYGHESWDK4MDS36HI/graph.json","events_json":"https://pith.science/api/pith-number/JQWMYM3DLIYGHESWDK4MDS36HI/events.json","paper":"https://pith.science/paper/JQWMYM3D"},"agent_actions":{"view_html":"https://pith.science/pith/JQWMYM3DLIYGHESWDK4MDS36HI","download_json":"https://pith.science/pith/JQWMYM3DLIYGHESWDK4MDS36HI.json","view_paper":"https://pith.science/paper/JQWMYM3D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.06680&json=true","fetch_graph":"https://pith.science/api/pith-number/JQWMYM3DLIYGHESWDK4MDS36HI/graph.json","fetch_events":"https://pith.science/api/pith-number/JQWMYM3DLIYGHESWDK4MDS36HI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JQWMYM3DLIYGHESWDK4MDS36HI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JQWMYM3DLIYGHESWDK4MDS36HI/action/storage_attestation","attest_author":"https://pith.science/pith/JQWMYM3DLIYGHESWDK4MDS36HI/action/author_attestation","sign_citation":"https://pith.science/pith/JQWMYM3DLIYGHESWDK4MDS36HI/action/citation_signature","submit_replication":"https://pith.science/pith/JQWMYM3DLIYGHESWDK4MDS36HI/action/replication_record"}},"created_at":"2026-05-18T00:13:44.404807+00:00","updated_at":"2026-05-18T00:13:44.404807+00:00"}