{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:DWRNELA336R3K4OM2BFPJRZ5EU","short_pith_number":"pith:DWRNELA3","schema_version":"1.0","canonical_sha256":"1da2d22c1bdfa3b571ccd04af4c73d2504b7af652be8926b92714600e48d26dd","source":{"kind":"arxiv","id":"1906.04574","version":1},"attestation_state":"computed","paper":{"title":"Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ayushi Gupta, Kuldeep Marotirao Biradar, Murari Mandal, Santosh Kumar Vipparthi","submitted_at":"2019-06-11T13:23:04Z","abstract_excerpt":"Time-stamp aware anomaly detection in traffic videos is an essential task for the advancement of the intelligent transportation system. Anomaly detection in videos is a challenging problem due to sparse occurrence of anomalous events, inconsistent behavior of a different type of anomalies and imbalanced available data for normal and abnormal scenarios. In this paper, we present a three-stage pipeline to learn the motion patterns in videos to detect a visual anomaly. First, the background is estimated from recent history frames to identify the motionless objects. This background image is used t"},"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":"1906.04574","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-11T13:23:04Z","cross_cats_sorted":[],"title_canon_sha256":"78d6d74917b78202b5a1927fabb150a9283432e243ea106f98aba66e223693fe","abstract_canon_sha256":"715a02a1526fe5fdcd82a91bd5b3d5503e4d2187ebcbb07b1bf34470cb081406"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:43:38.870013Z","signature_b64":"ow7MybPxYkvCb3PC5T2o3PkjWm6pU+GXYrlhFl+DGF5Mq1n+ZsscA/e64NNRIAyVk3ZKcRfvGBpYPb80RZg4Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1da2d22c1bdfa3b571ccd04af4c73d2504b7af652be8926b92714600e48d26dd","last_reissued_at":"2026-05-17T23:43:38.869331Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:43:38.869331Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Challenges in Time-Stamp Aware Anomaly Detection in Traffic Videos","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ayushi Gupta, Kuldeep Marotirao Biradar, Murari Mandal, Santosh Kumar Vipparthi","submitted_at":"2019-06-11T13:23:04Z","abstract_excerpt":"Time-stamp aware anomaly detection in traffic videos is an essential task for the advancement of the intelligent transportation system. Anomaly detection in videos is a challenging problem due to sparse occurrence of anomalous events, inconsistent behavior of a different type of anomalies and imbalanced available data for normal and abnormal scenarios. In this paper, we present a three-stage pipeline to learn the motion patterns in videos to detect a visual anomaly. First, the background is estimated from recent history frames to identify the motionless objects. This background image is used t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.04574","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":"1906.04574","created_at":"2026-05-17T23:43:38.869440+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.04574v1","created_at":"2026-05-17T23:43:38.869440+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.04574","created_at":"2026-05-17T23:43:38.869440+00:00"},{"alias_kind":"pith_short_12","alias_value":"DWRNELA336R3","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_16","alias_value":"DWRNELA336R3K4OM","created_at":"2026-05-18T12:33:15.570797+00:00"},{"alias_kind":"pith_short_8","alias_value":"DWRNELA3","created_at":"2026-05-18T12:33:15.570797+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/DWRNELA336R3K4OM2BFPJRZ5EU","json":"https://pith.science/pith/DWRNELA336R3K4OM2BFPJRZ5EU.json","graph_json":"https://pith.science/api/pith-number/DWRNELA336R3K4OM2BFPJRZ5EU/graph.json","events_json":"https://pith.science/api/pith-number/DWRNELA336R3K4OM2BFPJRZ5EU/events.json","paper":"https://pith.science/paper/DWRNELA3"},"agent_actions":{"view_html":"https://pith.science/pith/DWRNELA336R3K4OM2BFPJRZ5EU","download_json":"https://pith.science/pith/DWRNELA336R3K4OM2BFPJRZ5EU.json","view_paper":"https://pith.science/paper/DWRNELA3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.04574&json=true","fetch_graph":"https://pith.science/api/pith-number/DWRNELA336R3K4OM2BFPJRZ5EU/graph.json","fetch_events":"https://pith.science/api/pith-number/DWRNELA336R3K4OM2BFPJRZ5EU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DWRNELA336R3K4OM2BFPJRZ5EU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DWRNELA336R3K4OM2BFPJRZ5EU/action/storage_attestation","attest_author":"https://pith.science/pith/DWRNELA336R3K4OM2BFPJRZ5EU/action/author_attestation","sign_citation":"https://pith.science/pith/DWRNELA336R3K4OM2BFPJRZ5EU/action/citation_signature","submit_replication":"https://pith.science/pith/DWRNELA336R3K4OM2BFPJRZ5EU/action/replication_record"}},"created_at":"2026-05-17T23:43:38.869440+00:00","updated_at":"2026-05-17T23:43:38.869440+00:00"}