{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:BYRT77UOEFO4DWLLWY54AGVEXU","short_pith_number":"pith:BYRT77UO","canonical_record":{"source":{"id":"1903.11891","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T10:49:57Z","cross_cats_sorted":[],"title_canon_sha256":"b0c065e9fb0b9721da2cc29fabbaf05bea24255b597d16929e8dd61767a11d9d","abstract_canon_sha256":"4a6878a68ac5622c1b9152fa4da8a84064ef36e4c7b4ef5d9050b6ebe58dee4f"},"schema_version":"1.0"},"canonical_sha256":"0e233ffe8e215dc1d96bb63bc01aa4bd0df375e0f946c1dd31ab0275a01dd79f","source":{"kind":"arxiv","id":"1903.11891","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.11891","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"arxiv_version","alias_value":"1903.11891v1","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11891","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"pith_short_12","alias_value":"BYRT77UOEFO4","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"BYRT77UOEFO4DWLL","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"BYRT77UO","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:BYRT77UOEFO4DWLLWY54AGVEXU","target":"record","payload":{"canonical_record":{"source":{"id":"1903.11891","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T10:49:57Z","cross_cats_sorted":[],"title_canon_sha256":"b0c065e9fb0b9721da2cc29fabbaf05bea24255b597d16929e8dd61767a11d9d","abstract_canon_sha256":"4a6878a68ac5622c1b9152fa4da8a84064ef36e4c7b4ef5d9050b6ebe58dee4f"},"schema_version":"1.0"},"canonical_sha256":"0e233ffe8e215dc1d96bb63bc01aa4bd0df375e0f946c1dd31ab0275a01dd79f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:58.777873Z","signature_b64":"fwWVz85zxU0J4nj3AQgZB8swdbi3wDUORX/OKtDndUbaDudbk6tTP9ZufDp/9XWEOIQGEBQXNYHQtWIo+5EdAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0e233ffe8e215dc1d96bb63bc01aa4bd0df375e0f946c1dd31ab0275a01dd79f","last_reissued_at":"2026-05-17T23:49:58.777394Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:58.777394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.11891","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xtE+Vya9mxb2lEfI/sTaofZG6o+jLIvEEmQeoTtcD20qkAGVnaprtGl/gfJJP7qdQ/iLzNG5LO59SlieXq3FDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:58:12.280472Z"},"content_sha256":"25572a2cb0c9cba92fefaf63cc31275828be4e63fed1eeeadd580012770da0c6","schema_version":"1.0","event_id":"sha256:25572a2cb0c9cba92fefaf63cc31275828be4e63fed1eeeadd580012770da0c6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:BYRT77UOEFO4DWLLWY54AGVEXU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"AED-Net: An Abnormal Event Detection Network","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guangcun Shan, Hichem Snoussi, Tian Wang, Yi Zhou, Yuxin Chen, Zichen Miao","submitted_at":"2019-03-28T10:49:57Z","abstract_excerpt":"It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a self-supervised framework, abnormal event detection network (AED-Net), which is composed of PCAnet and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, PCAnet is trained to extract high-level semantics of crowd's situation. Next, kPCA,a one-class classifier, is trained to determine anomaly of the scene. In contrast to some prevailing deep learning methods,the framework is completely self-supervised "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11891","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:58Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UQLAZ42pwdtcAk1XMIQj9VB8q7NH84Zwv6jnMeLaiEpOJj8Y2DCP7X+7F4OwMNH4Z7pfGTuJUdnAWeGn7jNuBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:58:12.281166Z"},"content_sha256":"e15b717e73a472a637f4a3886e06ad31a736cb91bda92ff3338972dacc9d96a7","schema_version":"1.0","event_id":"sha256:e15b717e73a472a637f4a3886e06ad31a736cb91bda92ff3338972dacc9d96a7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BYRT77UOEFO4DWLLWY54AGVEXU/bundle.json","state_url":"https://pith.science/pith/BYRT77UOEFO4DWLLWY54AGVEXU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BYRT77UOEFO4DWLLWY54AGVEXU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-26T11:58:12Z","links":{"resolver":"https://pith.science/pith/BYRT77UOEFO4DWLLWY54AGVEXU","bundle":"https://pith.science/pith/BYRT77UOEFO4DWLLWY54AGVEXU/bundle.json","state":"https://pith.science/pith/BYRT77UOEFO4DWLLWY54AGVEXU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BYRT77UOEFO4DWLLWY54AGVEXU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:BYRT77UOEFO4DWLLWY54AGVEXU","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"4a6878a68ac5622c1b9152fa4da8a84064ef36e4c7b4ef5d9050b6ebe58dee4f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T10:49:57Z","title_canon_sha256":"b0c065e9fb0b9721da2cc29fabbaf05bea24255b597d16929e8dd61767a11d9d"},"schema_version":"1.0","source":{"id":"1903.11891","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.11891","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"arxiv_version","alias_value":"1903.11891v1","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.11891","created_at":"2026-05-17T23:49:58Z"},{"alias_kind":"pith_short_12","alias_value":"BYRT77UOEFO4","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"BYRT77UOEFO4DWLL","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"BYRT77UO","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:e15b717e73a472a637f4a3886e06ad31a736cb91bda92ff3338972dacc9d96a7","target":"graph","created_at":"2026-05-17T23:49:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"It is challenging to detect the anomaly in crowded scenes for quite a long time. In this paper, a self-supervised framework, abnormal event detection network (AED-Net), which is composed of PCAnet and kernel principal component analysis (kPCA), is proposed to address this problem. Using surveillance video sequences of different scenes as raw data, PCAnet is trained to extract high-level semantics of crowd's situation. Next, kPCA,a one-class classifier, is trained to determine anomaly of the scene. In contrast to some prevailing deep learning methods,the framework is completely self-supervised ","authors_text":"Guangcun Shan, Hichem Snoussi, Tian Wang, Yi Zhou, Yuxin Chen, Zichen Miao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T10:49:57Z","title":"AED-Net: An Abnormal Event Detection Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.11891","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:25572a2cb0c9cba92fefaf63cc31275828be4e63fed1eeeadd580012770da0c6","target":"record","created_at":"2026-05-17T23:49:58Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"4a6878a68ac5622c1b9152fa4da8a84064ef36e4c7b4ef5d9050b6ebe58dee4f","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-03-28T10:49:57Z","title_canon_sha256":"b0c065e9fb0b9721da2cc29fabbaf05bea24255b597d16929e8dd61767a11d9d"},"schema_version":"1.0","source":{"id":"1903.11891","kind":"arxiv","version":1}},"canonical_sha256":"0e233ffe8e215dc1d96bb63bc01aa4bd0df375e0f946c1dd31ab0275a01dd79f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0e233ffe8e215dc1d96bb63bc01aa4bd0df375e0f946c1dd31ab0275a01dd79f","first_computed_at":"2026-05-17T23:49:58.777394Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:58.777394Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fwWVz85zxU0J4nj3AQgZB8swdbi3wDUORX/OKtDndUbaDudbk6tTP9ZufDp/9XWEOIQGEBQXNYHQtWIo+5EdAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:58.777873Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.11891","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25572a2cb0c9cba92fefaf63cc31275828be4e63fed1eeeadd580012770da0c6","sha256:e15b717e73a472a637f4a3886e06ad31a736cb91bda92ff3338972dacc9d96a7"],"state_sha256":"b5892a59a682dcef5d6bf2c42069f344fdba7d453bf8b35d464d121612da515d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sH9KGNns8NuJtoXtDmrj5Hoj2OQzQSHX15gUk/M5lh9LZr1PSAYcTaL424Mhw57krKMt9JMD6q9c+yU3RHYtAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:58:12.284983Z","bundle_sha256":"ee4ed31b235fc2e208bc21dd66a2c5bdf679b46b101c6579a7cc321ce0c8a7a9"}}