{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:XA44K5ILGLIG4DATD5RVFFEAXO","short_pith_number":"pith:XA44K5IL","canonical_record":{"source":{"id":"2402.09865","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-15T10:47:44Z","cross_cats_sorted":[],"title_canon_sha256":"9e79e1bf30043c2a86c95168bbe16bea708e0d32b7db74ebb5070b2c9a83db95","abstract_canon_sha256":"ce371b60ac674b52f92c9b4149c64de159b0666442b77a2e250d06ca5575fa3d"},"schema_version":"1.0"},"canonical_sha256":"b839c5750b32d06e0c131f63529480bbb706398fe19ab8adc61282b8bd47bdfa","source":{"kind":"arxiv","id":"2402.09865","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.09865","created_at":"2026-07-05T09:51:31Z"},{"alias_kind":"arxiv_version","alias_value":"2402.09865v1","created_at":"2026-07-05T09:51:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.09865","created_at":"2026-07-05T09:51:31Z"},{"alias_kind":"pith_short_12","alias_value":"XA44K5ILGLIG","created_at":"2026-07-05T09:51:31Z"},{"alias_kind":"pith_short_16","alias_value":"XA44K5ILGLIG4DAT","created_at":"2026-07-05T09:51:31Z"},{"alias_kind":"pith_short_8","alias_value":"XA44K5IL","created_at":"2026-07-05T09:51:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:XA44K5ILGLIG4DATD5RVFFEAXO","target":"record","payload":{"canonical_record":{"source":{"id":"2402.09865","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-15T10:47:44Z","cross_cats_sorted":[],"title_canon_sha256":"9e79e1bf30043c2a86c95168bbe16bea708e0d32b7db74ebb5070b2c9a83db95","abstract_canon_sha256":"ce371b60ac674b52f92c9b4149c64de159b0666442b77a2e250d06ca5575fa3d"},"schema_version":"1.0"},"canonical_sha256":"b839c5750b32d06e0c131f63529480bbb706398fe19ab8adc61282b8bd47bdfa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:51:31.115071Z","signature_b64":"lFbImuX2BvFkKkK8W5MLtm8yCYmLAKNTBaSfWHyflou70BOxs5A6gNTkynRU9RVS6deayYkxZEQDnHjIyFQzBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b839c5750b32d06e0c131f63529480bbb706398fe19ab8adc61282b8bd47bdfa","last_reissued_at":"2026-07-05T09:51:31.114647Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:51:31.114647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.09865","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":"2026-07-05T09:51:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5H5bzcHmL7NKMxd/HhTN7T8nelWG3Xl7jkmMk1AwOXF/6WuRLCWVBDu47svIL/sfa3WKbiHF3Fahx34+OJNrDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T01:04:20.316555Z"},"content_sha256":"3ae1ede61c104d364254b81d0db9d9a9a65c125f154199212363d715e759ccfa","schema_version":"1.0","event_id":"sha256:3ae1ede61c104d364254b81d0db9d9a9a65c125f154199212363d715e759ccfa"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:XA44K5ILGLIG4DATD5RVFFEAXO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Andrija Petrovi\\'c, Mladen Nikoli\\'c, Momir Ad\\v{z}emovi\\'c, Predrag Tadi\\'c","submitted_at":"2024-02-15T10:47:44Z","abstract_excerpt":"Traditional tracking-by-detection systems typically employ Kalman filters (KF) for state estimation. However, the KF requires domain-specific design choices and it is ill-suited to handling non-linear motion patterns. To address these limitations, we propose two innovative data-driven filtering methods. Our first method employs a Bayesian filter with a trainable motion model to predict an object's future location and combines its predictions with observations gained from an object detector to enhance bounding box prediction accuracy. Moreover, it dispenses with most domain-specific design choi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.09865","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/2402.09865/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":"2026-07-05T09:51:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fIKGhknqJdHLh9UJkm9OPr+jmNnsxU/Fwy+ejgsd37OKkmfMkCgC2C3QnBPozQwhuSn4scWXBcqgRHcKRltdCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T01:04:20.317251Z"},"content_sha256":"8b9bbc8abbaffa6c9dadc3224dae53ee504182495615203081a2371eb641dd0a","schema_version":"1.0","event_id":"sha256:8b9bbc8abbaffa6c9dadc3224dae53ee504182495615203081a2371eb641dd0a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XA44K5ILGLIG4DATD5RVFFEAXO/bundle.json","state_url":"https://pith.science/pith/XA44K5ILGLIG4DATD5RVFFEAXO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XA44K5ILGLIG4DATD5RVFFEAXO/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":"2026-08-17T01:04:20Z","links":{"resolver":"https://pith.science/pith/XA44K5ILGLIG4DATD5RVFFEAXO","bundle":"https://pith.science/pith/XA44K5ILGLIG4DATD5RVFFEAXO/bundle.json","state":"https://pith.science/pith/XA44K5ILGLIG4DATD5RVFFEAXO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XA44K5ILGLIG4DATD5RVFFEAXO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:XA44K5ILGLIG4DATD5RVFFEAXO","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":"ce371b60ac674b52f92c9b4149c64de159b0666442b77a2e250d06ca5575fa3d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-15T10:47:44Z","title_canon_sha256":"9e79e1bf30043c2a86c95168bbe16bea708e0d32b7db74ebb5070b2c9a83db95"},"schema_version":"1.0","source":{"id":"2402.09865","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.09865","created_at":"2026-07-05T09:51:31Z"},{"alias_kind":"arxiv_version","alias_value":"2402.09865v1","created_at":"2026-07-05T09:51:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.09865","created_at":"2026-07-05T09:51:31Z"},{"alias_kind":"pith_short_12","alias_value":"XA44K5ILGLIG","created_at":"2026-07-05T09:51:31Z"},{"alias_kind":"pith_short_16","alias_value":"XA44K5ILGLIG4DAT","created_at":"2026-07-05T09:51:31Z"},{"alias_kind":"pith_short_8","alias_value":"XA44K5IL","created_at":"2026-07-05T09:51:31Z"}],"graph_snapshots":[{"event_id":"sha256:8b9bbc8abbaffa6c9dadc3224dae53ee504182495615203081a2371eb641dd0a","target":"graph","created_at":"2026-07-05T09:51:31Z","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/2402.09865/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Traditional tracking-by-detection systems typically employ Kalman filters (KF) for state estimation. However, the KF requires domain-specific design choices and it is ill-suited to handling non-linear motion patterns. To address these limitations, we propose two innovative data-driven filtering methods. Our first method employs a Bayesian filter with a trainable motion model to predict an object's future location and combines its predictions with observations gained from an object detector to enhance bounding box prediction accuracy. Moreover, it dispenses with most domain-specific design choi","authors_text":"Andrija Petrovi\\'c, Mladen Nikoli\\'c, Momir Ad\\v{z}emovi\\'c, Predrag Tadi\\'c","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-15T10:47:44Z","title":"Beyond Kalman Filters: Deep Learning-Based Filters for Improved Object Tracking"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.09865","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":"sha256:3ae1ede61c104d364254b81d0db9d9a9a65c125f154199212363d715e759ccfa","target":"record","created_at":"2026-07-05T09:51:31Z","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":"ce371b60ac674b52f92c9b4149c64de159b0666442b77a2e250d06ca5575fa3d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-02-15T10:47:44Z","title_canon_sha256":"9e79e1bf30043c2a86c95168bbe16bea708e0d32b7db74ebb5070b2c9a83db95"},"schema_version":"1.0","source":{"id":"2402.09865","kind":"arxiv","version":1}},"canonical_sha256":"b839c5750b32d06e0c131f63529480bbb706398fe19ab8adc61282b8bd47bdfa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b839c5750b32d06e0c131f63529480bbb706398fe19ab8adc61282b8bd47bdfa","first_computed_at":"2026-07-05T09:51:31.114647Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:51:31.114647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lFbImuX2BvFkKkK8W5MLtm8yCYmLAKNTBaSfWHyflou70BOxs5A6gNTkynRU9RVS6deayYkxZEQDnHjIyFQzBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:51:31.115071Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.09865","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3ae1ede61c104d364254b81d0db9d9a9a65c125f154199212363d715e759ccfa","sha256:8b9bbc8abbaffa6c9dadc3224dae53ee504182495615203081a2371eb641dd0a"],"state_sha256":"1817c541938b191e47e6fc88d3da8aa202cd8d26e97a2bb3c117f75660342c2f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TRes0tS2DENnE36KRRf+gvfBRKsbkNu43P3SQBvIt6WKL6xuwTaP5CBwLHOK8neJh3Jf34pl4hbodhk0iPGWBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T01:04:20.320381Z","bundle_sha256":"da44e198e444f8391f06d36990fa05a2664c6f9f2be4310457485207a14d6a33"}}