{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:Z3JBVB2LDJUNE6B334MLKS3ZD3","short_pith_number":"pith:Z3JBVB2L","canonical_record":{"source":{"id":"2101.08463","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-01-21T06:45:56Z","cross_cats_sorted":[],"title_canon_sha256":"a2f974d924c65bb5ec94d998146b2cccf1460690c89a96636e6e559129d69c2b","abstract_canon_sha256":"ab9d5afe95b01f34af133c4af1ccc565cf0ecfd313b09de4d229f27c22d3daf6"},"schema_version":"1.0"},"canonical_sha256":"ced21a874b1a68d2783bdf18b54b791ef2a4ff8d1a0172eea00bc7c575a0138f","source":{"kind":"arxiv","id":"2101.08463","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.08463","created_at":"2026-07-05T02:08:35Z"},{"alias_kind":"arxiv_version","alias_value":"2101.08463v1","created_at":"2026-07-05T02:08:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.08463","created_at":"2026-07-05T02:08:35Z"},{"alias_kind":"pith_short_12","alias_value":"Z3JBVB2LDJUN","created_at":"2026-07-05T02:08:35Z"},{"alias_kind":"pith_short_16","alias_value":"Z3JBVB2LDJUNE6B3","created_at":"2026-07-05T02:08:35Z"},{"alias_kind":"pith_short_8","alias_value":"Z3JBVB2L","created_at":"2026-07-05T02:08:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:Z3JBVB2LDJUNE6B334MLKS3ZD3","target":"record","payload":{"canonical_record":{"source":{"id":"2101.08463","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-01-21T06:45:56Z","cross_cats_sorted":[],"title_canon_sha256":"a2f974d924c65bb5ec94d998146b2cccf1460690c89a96636e6e559129d69c2b","abstract_canon_sha256":"ab9d5afe95b01f34af133c4af1ccc565cf0ecfd313b09de4d229f27c22d3daf6"},"schema_version":"1.0"},"canonical_sha256":"ced21a874b1a68d2783bdf18b54b791ef2a4ff8d1a0172eea00bc7c575a0138f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:08:35.563518Z","signature_b64":"UOtzeByp1aGeAC/UuGKGzpP7cfZbJ2K9HM9xg+Tvlh7Jx9/wnJWf1FInLziwQ2KMp97biDQTFB0Myilu90i/Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ced21a874b1a68d2783bdf18b54b791ef2a4ff8d1a0172eea00bc7c575a0138f","last_reissued_at":"2026-07-05T02:08:35.563131Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:08:35.563131Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2101.08463","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":"2026-07-05T02:08:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3B6IR/cIpAjrq6E6Er2OATozZb+KM7d8K1LHq8Iv+yyfGCmc87j5mDxvPVEnUPBfXcEabD5c3pgR7IHM8w1TAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:05:34.011607Z"},"content_sha256":"102f9454402e0b00b3377e34f712ee4996bcec6abb4692123c86011126624b0d","schema_version":"1.0","event_id":"sha256:102f9454402e0b00b3377e34f712ee4996bcec6abb4692123c86011126624b0d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:Z3JBVB2LDJUNE6B334MLKS3ZD3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Debarati B. Chakraborty, Deesha Chavan, Dev Saad","submitted_at":"2021-01-21T06:45:56Z","abstract_excerpt":"Predicting on-road abnormalities such as road accidents or traffic violations is a challenging task in traffic surveillance. If such predictions can be done in advance, many damages can be controlled. Here in our wok, we tried to formulate a solution for automated collision prediction in traffic surveillance videos with computer vision and deep networks. It involves object detection, tracking, trajectory estimation, and collision prediction. We propose an end-to-end collision prediction system, named as COLLIDE-PRED, that intelligently integrates the information of past and future trajectories"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.08463","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2101.08463/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T02:08:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NVyD48zriPYL27vfVLHJeGG2HJT67fczslC89IqvTtNndP/iOG5UfDniiL+NR9glfTlwoGroXZPEy4hoYTdPDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T12:05:34.012291Z"},"content_sha256":"79d8d37bf65b488c1d3169778ce94d5c736190db3ee6cd218a04c51587d2fe8d","schema_version":"1.0","event_id":"sha256:79d8d37bf65b488c1d3169778ce94d5c736190db3ee6cd218a04c51587d2fe8d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Z3JBVB2LDJUNE6B334MLKS3ZD3/bundle.json","state_url":"https://pith.science/pith/Z3JBVB2LDJUNE6B334MLKS3ZD3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Z3JBVB2LDJUNE6B334MLKS3ZD3/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":"2026-08-18T12:05:34Z","links":{"resolver":"https://pith.science/pith/Z3JBVB2LDJUNE6B334MLKS3ZD3","bundle":"https://pith.science/pith/Z3JBVB2LDJUNE6B334MLKS3ZD3/bundle.json","state":"https://pith.science/pith/Z3JBVB2LDJUNE6B334MLKS3ZD3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Z3JBVB2LDJUNE6B334MLKS3ZD3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:Z3JBVB2LDJUNE6B334MLKS3ZD3","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":"ab9d5afe95b01f34af133c4af1ccc565cf0ecfd313b09de4d229f27c22d3daf6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-01-21T06:45:56Z","title_canon_sha256":"a2f974d924c65bb5ec94d998146b2cccf1460690c89a96636e6e559129d69c2b"},"schema_version":"1.0","source":{"id":"2101.08463","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2101.08463","created_at":"2026-07-05T02:08:35Z"},{"alias_kind":"arxiv_version","alias_value":"2101.08463v1","created_at":"2026-07-05T02:08:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2101.08463","created_at":"2026-07-05T02:08:35Z"},{"alias_kind":"pith_short_12","alias_value":"Z3JBVB2LDJUN","created_at":"2026-07-05T02:08:35Z"},{"alias_kind":"pith_short_16","alias_value":"Z3JBVB2LDJUNE6B3","created_at":"2026-07-05T02:08:35Z"},{"alias_kind":"pith_short_8","alias_value":"Z3JBVB2L","created_at":"2026-07-05T02:08:35Z"}],"graph_snapshots":[{"event_id":"sha256:79d8d37bf65b488c1d3169778ce94d5c736190db3ee6cd218a04c51587d2fe8d","target":"graph","created_at":"2026-07-05T02:08:35Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2101.08463/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Predicting on-road abnormalities such as road accidents or traffic violations is a challenging task in traffic surveillance. If such predictions can be done in advance, many damages can be controlled. Here in our wok, we tried to formulate a solution for automated collision prediction in traffic surveillance videos with computer vision and deep networks. It involves object detection, tracking, trajectory estimation, and collision prediction. We propose an end-to-end collision prediction system, named as COLLIDE-PRED, that intelligently integrates the information of past and future trajectories","authors_text":"Debarati B. Chakraborty, Deesha Chavan, Dev Saad","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-01-21T06:45:56Z","title":"COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2101.08463","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":"sha256:102f9454402e0b00b3377e34f712ee4996bcec6abb4692123c86011126624b0d","target":"record","created_at":"2026-07-05T02:08:35Z","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":"ab9d5afe95b01f34af133c4af1ccc565cf0ecfd313b09de4d229f27c22d3daf6","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-01-21T06:45:56Z","title_canon_sha256":"a2f974d924c65bb5ec94d998146b2cccf1460690c89a96636e6e559129d69c2b"},"schema_version":"1.0","source":{"id":"2101.08463","kind":"arxiv","version":1}},"canonical_sha256":"ced21a874b1a68d2783bdf18b54b791ef2a4ff8d1a0172eea00bc7c575a0138f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ced21a874b1a68d2783bdf18b54b791ef2a4ff8d1a0172eea00bc7c575a0138f","first_computed_at":"2026-07-05T02:08:35.563131Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:08:35.563131Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UOtzeByp1aGeAC/UuGKGzpP7cfZbJ2K9HM9xg+Tvlh7Jx9/wnJWf1FInLziwQ2KMp97biDQTFB0Myilu90i/Cg==","signature_status":"signed_v1","signed_at":"2026-07-05T02:08:35.563518Z","signed_message":"canonical_sha256_bytes"},"source_id":"2101.08463","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:102f9454402e0b00b3377e34f712ee4996bcec6abb4692123c86011126624b0d","sha256:79d8d37bf65b488c1d3169778ce94d5c736190db3ee6cd218a04c51587d2fe8d"],"state_sha256":"d718cb4243db21dbdd41552f3df944db3c26c00558f685f77618ae18128a2a6a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p7AHzm/z/zW4kTqipiUHR6Gab0547Yya7/ZyFo+6svWIKGbzySRbfXCyYFu/0apfP0pk4ZlcY9PABjLfnb+PDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T12:05:34.021340Z","bundle_sha256":"a87bb45442fef275bd13e4a866a89a2324b3163cdee4aab1ff260e2817fb16e2"}}