{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:QS3BAH26X7JVVADXNLOBXTE36J","short_pith_number":"pith:QS3BAH26","schema_version":"1.0","canonical_sha256":"84b6101f5ebfd35a80776adc1bcc9bf247049a240cd492592ff3bda292e92b6b","source":{"kind":"arxiv","id":"1809.07357","version":1},"attestation_state":"computed","paper":{"title":"Combined Image- and World-Space Tracking in Traffic Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aljosa Osep, Bastian Leibe, Markus Mathias, Wolfgang Mehner","submitted_at":"2018-09-19T18:16:42Z","abstract_excerpt":"Tracking in urban street scenes plays a central role in autonomous systems such as self-driving cars. Most of the current vision-based tracking methods perform tracking in the image domain. Other approaches, eg based on LIDAR and radar, track purely in 3D. While some vision-based tracking methods invoke 3D information in parts of their pipeline, and some 3D-based methods utilize image-based information in components of their approach, we propose to use image- and world-space information jointly throughout our method. We present our tracking pipeline as a 3D extension of image-based tracking. F"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1809.07357","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-09-19T18:16:42Z","cross_cats_sorted":[],"title_canon_sha256":"a81c6890303d4242b3ac38c41aa4b0540ffe470f80567101b9cffa119dbe6c5a","abstract_canon_sha256":"a540fa53b336a7fe79d619be4120a79737b39d4c739eca51b00ac24f36a704ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:05:18.703130Z","signature_b64":"DY4ssh+7sKkxwKtxErXd1aCvO57CWR6J9XYMl5hLAB3qI5FBEZv0WJTvu8+TmXO4/6weFPg7E6gxcgRwT+rJDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84b6101f5ebfd35a80776adc1bcc9bf247049a240cd492592ff3bda292e92b6b","last_reissued_at":"2026-05-18T00:05:18.702493Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:05:18.702493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Combined Image- and World-Space Tracking in Traffic Scenes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Aljosa Osep, Bastian Leibe, Markus Mathias, Wolfgang Mehner","submitted_at":"2018-09-19T18:16:42Z","abstract_excerpt":"Tracking in urban street scenes plays a central role in autonomous systems such as self-driving cars. Most of the current vision-based tracking methods perform tracking in the image domain. Other approaches, eg based on LIDAR and radar, track purely in 3D. While some vision-based tracking methods invoke 3D information in parts of their pipeline, and some 3D-based methods utilize image-based information in components of their approach, we propose to use image- and world-space information jointly throughout our method. We present our tracking pipeline as a 3D extension of image-based tracking. F"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.07357","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1809.07357","created_at":"2026-05-18T00:05:18.702575+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.07357v1","created_at":"2026-05-18T00:05:18.702575+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.07357","created_at":"2026-05-18T00:05:18.702575+00:00"},{"alias_kind":"pith_short_12","alias_value":"QS3BAH26X7JV","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_16","alias_value":"QS3BAH26X7JVVADX","created_at":"2026-05-18T12:32:46.962924+00:00"},{"alias_kind":"pith_short_8","alias_value":"QS3BAH26","created_at":"2026-05-18T12:32:46.962924+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QS3BAH26X7JVVADXNLOBXTE36J","json":"https://pith.science/pith/QS3BAH26X7JVVADXNLOBXTE36J.json","graph_json":"https://pith.science/api/pith-number/QS3BAH26X7JVVADXNLOBXTE36J/graph.json","events_json":"https://pith.science/api/pith-number/QS3BAH26X7JVVADXNLOBXTE36J/events.json","paper":"https://pith.science/paper/QS3BAH26"},"agent_actions":{"view_html":"https://pith.science/pith/QS3BAH26X7JVVADXNLOBXTE36J","download_json":"https://pith.science/pith/QS3BAH26X7JVVADXNLOBXTE36J.json","view_paper":"https://pith.science/paper/QS3BAH26","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.07357&json=true","fetch_graph":"https://pith.science/api/pith-number/QS3BAH26X7JVVADXNLOBXTE36J/graph.json","fetch_events":"https://pith.science/api/pith-number/QS3BAH26X7JVVADXNLOBXTE36J/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QS3BAH26X7JVVADXNLOBXTE36J/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QS3BAH26X7JVVADXNLOBXTE36J/action/storage_attestation","attest_author":"https://pith.science/pith/QS3BAH26X7JVVADXNLOBXTE36J/action/author_attestation","sign_citation":"https://pith.science/pith/QS3BAH26X7JVVADXNLOBXTE36J/action/citation_signature","submit_replication":"https://pith.science/pith/QS3BAH26X7JVVADXNLOBXTE36J/action/replication_record"}},"created_at":"2026-05-18T00:05:18.702575+00:00","updated_at":"2026-05-18T00:05:18.702575+00:00"}