{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:N462GFOXF6U6YLLO5UAETGIRTB","short_pith_number":"pith:N462GFOX","canonical_record":{"source":{"id":"1907.05315","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-11T15:43:38Z","cross_cats_sorted":[],"title_canon_sha256":"e3af8072d675a9ec177fd80c995502ff107912b8eb6ffdc42be80ae9d4f594dd","abstract_canon_sha256":"99d4c5e4be16ef7f76dc7ebd629239ed1130be90d3439d7fb3f139c439cdfdc7"},"schema_version":"1.0"},"canonical_sha256":"6f3da315d72fa9ec2d6eed00499911986149245df5bdfb4d2c2f961c0bc954c6","source":{"kind":"arxiv","id":"1907.05315","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.05315","created_at":"2026-05-17T23:40:50Z"},{"alias_kind":"arxiv_version","alias_value":"1907.05315v1","created_at":"2026-05-17T23:40:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.05315","created_at":"2026-05-17T23:40:50Z"},{"alias_kind":"pith_short_12","alias_value":"N462GFOXF6U6","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"N462GFOXF6U6YLLO","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"N462GFOX","created_at":"2026-05-18T12:33:24Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:N462GFOXF6U6YLLO5UAETGIRTB","target":"record","payload":{"canonical_record":{"source":{"id":"1907.05315","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-11T15:43:38Z","cross_cats_sorted":[],"title_canon_sha256":"e3af8072d675a9ec177fd80c995502ff107912b8eb6ffdc42be80ae9d4f594dd","abstract_canon_sha256":"99d4c5e4be16ef7f76dc7ebd629239ed1130be90d3439d7fb3f139c439cdfdc7"},"schema_version":"1.0"},"canonical_sha256":"6f3da315d72fa9ec2d6eed00499911986149245df5bdfb4d2c2f961c0bc954c6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:40:50.774343Z","signature_b64":"JpQ87Kcy5DOCNRnDFWGDj5//D/VIPgIr4WnmfdBqyywDzCJhKoyOjW5eQ2V3VDetZwSJIu767io+sukeimofAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6f3da315d72fa9ec2d6eed00499911986149245df5bdfb4d2c2f961c0bc954c6","last_reissued_at":"2026-05-17T23:40:50.773612Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:40:50.773612Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.05315","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":"2026-05-17T23:40:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2eQ/2pUrAjaATTsWjB1RklfbwMNpUgpS423vX5W9waRvOo+QJAykx6qrOzmT2t82TxNkcKt/sHYERgDdyTKIBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T04:31:04.900115Z"},"content_sha256":"6be57325e03042a86e2065c89aac4ae18307d6bbb9ad02c9fff2169f7403fb99","schema_version":"1.0","event_id":"sha256:6be57325e03042a86e2065c89aac4ae18307d6bbb9ad02c9fff2169f7403fb99"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:N462GFOXF6U6YLLO5UAETGIRTB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Peizhao Li, Xiantong Zhen, Xiaolong Jiang, Yanjing Li","submitted_at":"2019-07-11T15:43:38Z","abstract_excerpt":"In this work, we present an end-to-end framework to settle data association in online Multiple-Object Tracking (MOT). Given detection responses, we formulate the frame-by-frame data association as Maximum Weighted Bipartite Matching problem, whose solution is learned using a neural network. The network incorporates an affinity learning module, wherein both appearance and motion cues are investigated to encode object feature representation and compute pairwise affinities. Employing the computed affinities as edge weights, the following matching problem on a bipartite graph is resolved by the op"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.05315","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":"2026-05-17T23:40:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0CrkGZKE04VFIbN7DTLeE/Jnqzemgmhr5FOx04qMQ1PbkR78rYBp+JL4mLGm5s73vFCAj0pKRMjrcSAmpcbKBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T04:31:04.900836Z"},"content_sha256":"fe018d79e2e3c083c2921fee7f82767616866ae06ecc019769a4afdb6e68af0a","schema_version":"1.0","event_id":"sha256:fe018d79e2e3c083c2921fee7f82767616866ae06ecc019769a4afdb6e68af0a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/N462GFOXF6U6YLLO5UAETGIRTB/bundle.json","state_url":"https://pith.science/pith/N462GFOXF6U6YLLO5UAETGIRTB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/N462GFOXF6U6YLLO5UAETGIRTB/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":"2026-05-26T04:31:04Z","links":{"resolver":"https://pith.science/pith/N462GFOXF6U6YLLO5UAETGIRTB","bundle":"https://pith.science/pith/N462GFOXF6U6YLLO5UAETGIRTB/bundle.json","state":"https://pith.science/pith/N462GFOXF6U6YLLO5UAETGIRTB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/N462GFOXF6U6YLLO5UAETGIRTB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:N462GFOXF6U6YLLO5UAETGIRTB","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":"99d4c5e4be16ef7f76dc7ebd629239ed1130be90d3439d7fb3f139c439cdfdc7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-11T15:43:38Z","title_canon_sha256":"e3af8072d675a9ec177fd80c995502ff107912b8eb6ffdc42be80ae9d4f594dd"},"schema_version":"1.0","source":{"id":"1907.05315","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.05315","created_at":"2026-05-17T23:40:50Z"},{"alias_kind":"arxiv_version","alias_value":"1907.05315v1","created_at":"2026-05-17T23:40:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.05315","created_at":"2026-05-17T23:40:50Z"},{"alias_kind":"pith_short_12","alias_value":"N462GFOXF6U6","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"N462GFOXF6U6YLLO","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"N462GFOX","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:fe018d79e2e3c083c2921fee7f82767616866ae06ecc019769a4afdb6e68af0a","target":"graph","created_at":"2026-05-17T23:40:50Z","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 this work, we present an end-to-end framework to settle data association in online Multiple-Object Tracking (MOT). Given detection responses, we formulate the frame-by-frame data association as Maximum Weighted Bipartite Matching problem, whose solution is learned using a neural network. The network incorporates an affinity learning module, wherein both appearance and motion cues are investigated to encode object feature representation and compute pairwise affinities. Employing the computed affinities as edge weights, the following matching problem on a bipartite graph is resolved by the op","authors_text":"Peizhao Li, Xiantong Zhen, Xiaolong Jiang, Yanjing Li","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-11T15:43:38Z","title":"Graph Neural Based End-to-end Data Association Framework for Online Multiple-Object Tracking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.05315","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":"sha256:6be57325e03042a86e2065c89aac4ae18307d6bbb9ad02c9fff2169f7403fb99","target":"record","created_at":"2026-05-17T23:40:50Z","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":"99d4c5e4be16ef7f76dc7ebd629239ed1130be90d3439d7fb3f139c439cdfdc7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-11T15:43:38Z","title_canon_sha256":"e3af8072d675a9ec177fd80c995502ff107912b8eb6ffdc42be80ae9d4f594dd"},"schema_version":"1.0","source":{"id":"1907.05315","kind":"arxiv","version":1}},"canonical_sha256":"6f3da315d72fa9ec2d6eed00499911986149245df5bdfb4d2c2f961c0bc954c6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6f3da315d72fa9ec2d6eed00499911986149245df5bdfb4d2c2f961c0bc954c6","first_computed_at":"2026-05-17T23:40:50.773612Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:40:50.773612Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JpQ87Kcy5DOCNRnDFWGDj5//D/VIPgIr4WnmfdBqyywDzCJhKoyOjW5eQ2V3VDetZwSJIu767io+sukeimofAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:40:50.774343Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.05315","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6be57325e03042a86e2065c89aac4ae18307d6bbb9ad02c9fff2169f7403fb99","sha256:fe018d79e2e3c083c2921fee7f82767616866ae06ecc019769a4afdb6e68af0a"],"state_sha256":"cde403fdcddfff4190e3592ae8361a4ff9461b4afedcf31a74a91ebfed20e656"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"plCUdI7mp7huTi04ohENlFgogfTv9OVWABe9ksP77J4BCIxE/x0DHhsaPKB2e26ZZhyTGpR7L2zhh9AiYxOgCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T04:31:04.904058Z","bundle_sha256":"ea0660d1d70b6a22e1c4980b55d08d865a2a4e359935061b331854e6d881eb28"}}