{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:IMBCW6WTL6JV45LIJ27CBINCZ4","short_pith_number":"pith:IMBCW6WT","canonical_record":{"source":{"id":"1801.00476","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-01T16:58:25Z","cross_cats_sorted":[],"title_canon_sha256":"b57b650c81f85197dcc72c42ea0ac3d457f4e353ffb52c74bfc5418c901bd99a","abstract_canon_sha256":"3dc07305c331331b1c56e6b8f4cd38265c79e0b9862024ccde32761fa5736e43"},"schema_version":"1.0"},"canonical_sha256":"43022b7ad35f935e75684ebe20a1a2cf08c7241a14f6a86ca937d578190945fe","source":{"kind":"arxiv","id":"1801.00476","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.00476","created_at":"2026-05-18T00:26:53Z"},{"alias_kind":"arxiv_version","alias_value":"1801.00476v1","created_at":"2026-05-18T00:26:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.00476","created_at":"2026-05-18T00:26:53Z"},{"alias_kind":"pith_short_12","alias_value":"IMBCW6WTL6JV","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IMBCW6WTL6JV45LI","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IMBCW6WT","created_at":"2026-05-18T12:32:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:IMBCW6WTL6JV45LIJ27CBINCZ4","target":"record","payload":{"canonical_record":{"source":{"id":"1801.00476","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-01T16:58:25Z","cross_cats_sorted":[],"title_canon_sha256":"b57b650c81f85197dcc72c42ea0ac3d457f4e353ffb52c74bfc5418c901bd99a","abstract_canon_sha256":"3dc07305c331331b1c56e6b8f4cd38265c79e0b9862024ccde32761fa5736e43"},"schema_version":"1.0"},"canonical_sha256":"43022b7ad35f935e75684ebe20a1a2cf08c7241a14f6a86ca937d578190945fe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:26:53.253239Z","signature_b64":"bopQ6PFOd04Ti8axio1sZ0HKri87t3Rt8UYt7tYMYLxil/0y31jT5qHUjOzaWQSZq11omOw1gOCU9al5D9m2BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"43022b7ad35f935e75684ebe20a1a2cf08c7241a14f6a86ca937d578190945fe","last_reissued_at":"2026-05-18T00:26:53.252702Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:26:53.252702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.00476","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":"2026-05-18T00:26:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EIjvpGFFXL4MeYYHCQ1tX4jQLZerqvFFA7ng/ZDP0zEFgMRDfUKHn48CzF4RhF5h93QceY/BuVTXJeHEEAKsAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T17:16:01.128882Z"},"content_sha256":"3f1eaea09bf7c32bd34fb6d1e6af4828c128ce07b83dc483ae224ca370eb8d01","schema_version":"1.0","event_id":"sha256:3f1eaea09bf7c32bd34fb6d1e6af4828c128ce07b83dc483ae224ca370eb8d01"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:IMBCW6WTL6JV45LIJ27CBINCZ4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Aggregated Channels Network for Real-Time Pedestrian Detection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alessandro Colombo, Anton Kummert, Farzin Ghorban, Javier Mar\\'in, Yu Su","submitted_at":"2018-01-01T16:58:25Z","abstract_excerpt":"Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware. In order to alleviate this drawback, most strategies focus on using a two-stage cascade approach. Essentially, in the first stage a fast method generates a significant but reduced amount of high quality proposals that later, in the second stage, are evaluated by the CNN. In this work, we propose a novel detection pipeline th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.00476","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":"2026-05-18T00:26:53Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CIO7RG8K18unRSvLBCZ3vgbmeWhB6FZ+nNAAJw5x/O4BJ5npP24ApHXi12/Byq0a/xG/jfQ8wD3V/i8bS644Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T17:16:01.129228Z"},"content_sha256":"8cfc6f2dbb5f3ce813b9bcc14d83b6a2abe9bc5dce3c5f1a2d9d31023a0c914e","schema_version":"1.0","event_id":"sha256:8cfc6f2dbb5f3ce813b9bcc14d83b6a2abe9bc5dce3c5f1a2d9d31023a0c914e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IMBCW6WTL6JV45LIJ27CBINCZ4/bundle.json","state_url":"https://pith.science/pith/IMBCW6WTL6JV45LIJ27CBINCZ4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IMBCW6WTL6JV45LIJ27CBINCZ4/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":"2026-06-03T17:16:01Z","links":{"resolver":"https://pith.science/pith/IMBCW6WTL6JV45LIJ27CBINCZ4","bundle":"https://pith.science/pith/IMBCW6WTL6JV45LIJ27CBINCZ4/bundle.json","state":"https://pith.science/pith/IMBCW6WTL6JV45LIJ27CBINCZ4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IMBCW6WTL6JV45LIJ27CBINCZ4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:IMBCW6WTL6JV45LIJ27CBINCZ4","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":"3dc07305c331331b1c56e6b8f4cd38265c79e0b9862024ccde32761fa5736e43","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-01T16:58:25Z","title_canon_sha256":"b57b650c81f85197dcc72c42ea0ac3d457f4e353ffb52c74bfc5418c901bd99a"},"schema_version":"1.0","source":{"id":"1801.00476","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.00476","created_at":"2026-05-18T00:26:53Z"},{"alias_kind":"arxiv_version","alias_value":"1801.00476v1","created_at":"2026-05-18T00:26:53Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.00476","created_at":"2026-05-18T00:26:53Z"},{"alias_kind":"pith_short_12","alias_value":"IMBCW6WTL6JV","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_16","alias_value":"IMBCW6WTL6JV45LI","created_at":"2026-05-18T12:32:31Z"},{"alias_kind":"pith_short_8","alias_value":"IMBCW6WT","created_at":"2026-05-18T12:32:31Z"}],"graph_snapshots":[{"event_id":"sha256:8cfc6f2dbb5f3ce813b9bcc14d83b6a2abe9bc5dce3c5f1a2d9d31023a0c914e","target":"graph","created_at":"2026-05-18T00:26:53Z","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":"Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware. In order to alleviate this drawback, most strategies focus on using a two-stage cascade approach. Essentially, in the first stage a fast method generates a significant but reduced amount of high quality proposals that later, in the second stage, are evaluated by the CNN. In this work, we propose a novel detection pipeline th","authors_text":"Alessandro Colombo, Anton Kummert, Farzin Ghorban, Javier Mar\\'in, Yu Su","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-01T16:58:25Z","title":"Aggregated Channels Network for Real-Time Pedestrian Detection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.00476","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":"sha256:3f1eaea09bf7c32bd34fb6d1e6af4828c128ce07b83dc483ae224ca370eb8d01","target":"record","created_at":"2026-05-18T00:26:53Z","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":"3dc07305c331331b1c56e6b8f4cd38265c79e0b9862024ccde32761fa5736e43","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-01T16:58:25Z","title_canon_sha256":"b57b650c81f85197dcc72c42ea0ac3d457f4e353ffb52c74bfc5418c901bd99a"},"schema_version":"1.0","source":{"id":"1801.00476","kind":"arxiv","version":1}},"canonical_sha256":"43022b7ad35f935e75684ebe20a1a2cf08c7241a14f6a86ca937d578190945fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"43022b7ad35f935e75684ebe20a1a2cf08c7241a14f6a86ca937d578190945fe","first_computed_at":"2026-05-18T00:26:53.252702Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:26:53.252702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bopQ6PFOd04Ti8axio1sZ0HKri87t3Rt8UYt7tYMYLxil/0y31jT5qHUjOzaWQSZq11omOw1gOCU9al5D9m2BQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:26:53.253239Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.00476","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3f1eaea09bf7c32bd34fb6d1e6af4828c128ce07b83dc483ae224ca370eb8d01","sha256:8cfc6f2dbb5f3ce813b9bcc14d83b6a2abe9bc5dce3c5f1a2d9d31023a0c914e"],"state_sha256":"dbd1c87f2632bf967e7677a133a9920d031229891154d7dc8dc5dec4da650b3a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IIXC9Io7bTK45uYmGwSinMXS4clxZc6H0cznRuwzPe3FPW7baClSLcavpEWDQ3qD6QNM17Dt5SQLhS3Pg9ivBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T17:16:01.131074Z","bundle_sha256":"cac6aeadfaff82dd5328fc98979162d36bbee83fe316b18c769d5f682888b790"}}