{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:O5QPXPNHD5BDHN6J4AQCMFY2XO","short_pith_number":"pith:O5QPXPNH","canonical_record":{"source":{"id":"1805.10620","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-27T13:16:40Z","cross_cats_sorted":[],"title_canon_sha256":"32fd5d586c3d03bbff3fbae8e353d39bb614626e93cac7e22133e8afe303738a","abstract_canon_sha256":"fefc463a56f2e6260053ee478a36e9e8cea6453a06cd7e947468602d53fd3460"},"schema_version":"1.0"},"canonical_sha256":"7760fbbda71f4233b7c9e02026171abb9f31b23ea6629b262560d9969b21c074","source":{"kind":"arxiv","id":"1805.10620","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10620","created_at":"2026-05-18T00:14:51Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10620v1","created_at":"2026-05-18T00:14:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10620","created_at":"2026-05-18T00:14:51Z"},{"alias_kind":"pith_short_12","alias_value":"O5QPXPNHD5BD","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"O5QPXPNHD5BDHN6J","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"O5QPXPNH","created_at":"2026-05-18T12:32:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:O5QPXPNHD5BDHN6J4AQCMFY2XO","target":"record","payload":{"canonical_record":{"source":{"id":"1805.10620","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-27T13:16:40Z","cross_cats_sorted":[],"title_canon_sha256":"32fd5d586c3d03bbff3fbae8e353d39bb614626e93cac7e22133e8afe303738a","abstract_canon_sha256":"fefc463a56f2e6260053ee478a36e9e8cea6453a06cd7e947468602d53fd3460"},"schema_version":"1.0"},"canonical_sha256":"7760fbbda71f4233b7c9e02026171abb9f31b23ea6629b262560d9969b21c074","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:51.870601Z","signature_b64":"S1gLK0uJJJG6XfqRVw9PdW2D07FLi0xHKB/+RPrS+NiAhWUP7IbnGm3Jy+hKz7k/93nfSSrPkF3jiFjhY6TkBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7760fbbda71f4233b7c9e02026171abb9f31b23ea6629b262560d9969b21c074","last_reissued_at":"2026-05-18T00:14:51.869819Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:51.869819Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.10620","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":"2026-05-18T00:14:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VvZi5HCXCANj3PhiR6efF009TAtKWQnLeNzBtCLgqbQq12K3GfVZfifAwHiyBiZQsDaqHROQZeN/podmm/OyBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:41:23.192583Z"},"content_sha256":"3e26f8d672f0aea21a2b98f513d09c1b2205df32bcb4b0a0b23c9837f5d77703","schema_version":"1.0","event_id":"sha256:3e26f8d672f0aea21a2b98f513d09c1b2205df32bcb4b0a0b23c9837f5d77703"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:O5QPXPNHD5BDHN6J4AQCMFY2XO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jiulong Zhang, Su Yang, Weishan Zhang, Xinfeng Zhang, Xinjian Zhang","submitted_at":"2018-05-27T13:16:40Z","abstract_excerpt":"In crowded scenes, detection and localization of abnormal behaviors is challenging in that high-density people make object segmentation and tracking extremely difficult. We associate the optical flows of multiple frames to capture short-term trajectories and introduce the histogram-based shape descriptor referred to as shape contexts to describe such short-term trajectories. Furthermore, we propose a K-NN similarity-based statistical model to detect anomalies over time and space, which is an unsupervised one-class learning algorithm requiring no clustering nor any prior assumption. Firstly, we"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10620","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":"2026-05-18T00:14:51Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TgbXf8IYx3kfM/14M47cJQ2EaBv/Fgk2tC4wmsXwREpC7bt9keP7ijvsaU6h+oV/Pibu/x+6Ld4rekO749bwBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T17:41:23.193296Z"},"content_sha256":"58b80f2013fc6d327b5001eb83d93741f5bda9220b3a079ad81cd69bcd615543","schema_version":"1.0","event_id":"sha256:58b80f2013fc6d327b5001eb83d93741f5bda9220b3a079ad81cd69bcd615543"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O5QPXPNHD5BDHN6J4AQCMFY2XO/bundle.json","state_url":"https://pith.science/pith/O5QPXPNHD5BDHN6J4AQCMFY2XO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O5QPXPNHD5BDHN6J4AQCMFY2XO/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":"2026-06-07T17:41:23Z","links":{"resolver":"https://pith.science/pith/O5QPXPNHD5BDHN6J4AQCMFY2XO","bundle":"https://pith.science/pith/O5QPXPNHD5BDHN6J4AQCMFY2XO/bundle.json","state":"https://pith.science/pith/O5QPXPNHD5BDHN6J4AQCMFY2XO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O5QPXPNHD5BDHN6J4AQCMFY2XO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:O5QPXPNHD5BDHN6J4AQCMFY2XO","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":"fefc463a56f2e6260053ee478a36e9e8cea6453a06cd7e947468602d53fd3460","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-27T13:16:40Z","title_canon_sha256":"32fd5d586c3d03bbff3fbae8e353d39bb614626e93cac7e22133e8afe303738a"},"schema_version":"1.0","source":{"id":"1805.10620","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.10620","created_at":"2026-05-18T00:14:51Z"},{"alias_kind":"arxiv_version","alias_value":"1805.10620v1","created_at":"2026-05-18T00:14:51Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.10620","created_at":"2026-05-18T00:14:51Z"},{"alias_kind":"pith_short_12","alias_value":"O5QPXPNHD5BD","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_16","alias_value":"O5QPXPNHD5BDHN6J","created_at":"2026-05-18T12:32:43Z"},{"alias_kind":"pith_short_8","alias_value":"O5QPXPNH","created_at":"2026-05-18T12:32:43Z"}],"graph_snapshots":[{"event_id":"sha256:58b80f2013fc6d327b5001eb83d93741f5bda9220b3a079ad81cd69bcd615543","target":"graph","created_at":"2026-05-18T00:14:51Z","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":"In crowded scenes, detection and localization of abnormal behaviors is challenging in that high-density people make object segmentation and tracking extremely difficult. We associate the optical flows of multiple frames to capture short-term trajectories and introduce the histogram-based shape descriptor referred to as shape contexts to describe such short-term trajectories. Furthermore, we propose a K-NN similarity-based statistical model to detect anomalies over time and space, which is an unsupervised one-class learning algorithm requiring no clustering nor any prior assumption. Firstly, we","authors_text":"Jiulong Zhang, Su Yang, Weishan Zhang, Xinfeng Zhang, Xinjian Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-27T13:16:40Z","title":"Anomaly Detection and Localization in Crowded Scenes by Motion-field Shape Description and Similarity-based Statistical Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.10620","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":"sha256:3e26f8d672f0aea21a2b98f513d09c1b2205df32bcb4b0a0b23c9837f5d77703","target":"record","created_at":"2026-05-18T00:14:51Z","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":"fefc463a56f2e6260053ee478a36e9e8cea6453a06cd7e947468602d53fd3460","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-05-27T13:16:40Z","title_canon_sha256":"32fd5d586c3d03bbff3fbae8e353d39bb614626e93cac7e22133e8afe303738a"},"schema_version":"1.0","source":{"id":"1805.10620","kind":"arxiv","version":1}},"canonical_sha256":"7760fbbda71f4233b7c9e02026171abb9f31b23ea6629b262560d9969b21c074","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7760fbbda71f4233b7c9e02026171abb9f31b23ea6629b262560d9969b21c074","first_computed_at":"2026-05-18T00:14:51.869819Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:51.869819Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"S1gLK0uJJJG6XfqRVw9PdW2D07FLi0xHKB/+RPrS+NiAhWUP7IbnGm3Jy+hKz7k/93nfSSrPkF3jiFjhY6TkBA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:51.870601Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.10620","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3e26f8d672f0aea21a2b98f513d09c1b2205df32bcb4b0a0b23c9837f5d77703","sha256:58b80f2013fc6d327b5001eb83d93741f5bda9220b3a079ad81cd69bcd615543"],"state_sha256":"dbd7a0372637d9768e132f918472587d2050d8633d5267eafa4ea235475d0673"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y+bQLaRZzwIgrFW9iSiHIMzaP8u3J7w9d8FjQEeeflr3baglVyvb3y2bQC1KWmqVwmAz0CLDSbTcaZ7g+2yaCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T17:41:23.197067Z","bundle_sha256":"0451f3647b8d4f198b381ae0562d868f912742375449f5d88ee9f4c3250b3ecc"}}