{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:7B5QVX7AB2ZGR7ZKTUSDRWVHOL","short_pith_number":"pith:7B5QVX7A","schema_version":"1.0","canonical_sha256":"f87b0adfe00eb268ff2a9d2438daa772ee25a8e118a06708827bbd7fe1214966","source":{"kind":"arxiv","id":"1902.01739","version":2},"attestation_state":"computed","paper":{"title":"An RNN-based IMM Filter Surrogate","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Michael Arens, Ronny Hug, Stefan Becker, Wolfgang H\\\"ubner","submitted_at":"2019-02-05T15:21:53Z","abstract_excerpt":"The problem of varying dynamics of tracked objects, such as pedestrians, is traditionally tackled with approaches like the Interacting Multiple Model (IMM) filter using a Bayesian formulation. By following the current trend towards using deep neural networks, in this paper an RNN-based IMM filter surrogate is presented. Similar to an IMM filter solution, the presented RNN-based model assigns a probability value to a performed dynamic and, based on them, puts out a multi-modal distribution over future pedestrian trajectories. The evaluation is done on synthetic data, reflecting prototypical ped"},"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":"1902.01739","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-02-05T15:21:53Z","cross_cats_sorted":[],"title_canon_sha256":"b1eeb3dad02854831fe75008773b3c2fb56da1e42482a65f262d15fb7f031567","abstract_canon_sha256":"6468d0bf4ae2ba66b6e31449a092113e395590afc77895d255feb003975a501d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:43.833370Z","signature_b64":"/baItOlMQg4kP/vaFbjGzKezoVlyhpqb5TXgPVhBdUiCO9wAU4KZxyDjFW72HAby5Sx5ZJZdW5/Y4VX+CB2fAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f87b0adfe00eb268ff2a9d2438daa772ee25a8e118a06708827bbd7fe1214966","last_reissued_at":"2026-05-17T23:47:43.832781Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:43.832781Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An RNN-based IMM Filter Surrogate","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Michael Arens, Ronny Hug, Stefan Becker, Wolfgang H\\\"ubner","submitted_at":"2019-02-05T15:21:53Z","abstract_excerpt":"The problem of varying dynamics of tracked objects, such as pedestrians, is traditionally tackled with approaches like the Interacting Multiple Model (IMM) filter using a Bayesian formulation. By following the current trend towards using deep neural networks, in this paper an RNN-based IMM filter surrogate is presented. Similar to an IMM filter solution, the presented RNN-based model assigns a probability value to a performed dynamic and, based on them, puts out a multi-modal distribution over future pedestrian trajectories. The evaluation is done on synthetic data, reflecting prototypical ped"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.01739","kind":"arxiv","version":2},"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":"1902.01739","created_at":"2026-05-17T23:47:43.832873+00:00"},{"alias_kind":"arxiv_version","alias_value":"1902.01739v2","created_at":"2026-05-17T23:47:43.832873+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.01739","created_at":"2026-05-17T23:47:43.832873+00:00"},{"alias_kind":"pith_short_12","alias_value":"7B5QVX7AB2ZG","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"7B5QVX7AB2ZGR7ZK","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"7B5QVX7A","created_at":"2026-05-18T12:33:12.712433+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/7B5QVX7AB2ZGR7ZKTUSDRWVHOL","json":"https://pith.science/pith/7B5QVX7AB2ZGR7ZKTUSDRWVHOL.json","graph_json":"https://pith.science/api/pith-number/7B5QVX7AB2ZGR7ZKTUSDRWVHOL/graph.json","events_json":"https://pith.science/api/pith-number/7B5QVX7AB2ZGR7ZKTUSDRWVHOL/events.json","paper":"https://pith.science/paper/7B5QVX7A"},"agent_actions":{"view_html":"https://pith.science/pith/7B5QVX7AB2ZGR7ZKTUSDRWVHOL","download_json":"https://pith.science/pith/7B5QVX7AB2ZGR7ZKTUSDRWVHOL.json","view_paper":"https://pith.science/paper/7B5QVX7A","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1902.01739&json=true","fetch_graph":"https://pith.science/api/pith-number/7B5QVX7AB2ZGR7ZKTUSDRWVHOL/graph.json","fetch_events":"https://pith.science/api/pith-number/7B5QVX7AB2ZGR7ZKTUSDRWVHOL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7B5QVX7AB2ZGR7ZKTUSDRWVHOL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7B5QVX7AB2ZGR7ZKTUSDRWVHOL/action/storage_attestation","attest_author":"https://pith.science/pith/7B5QVX7AB2ZGR7ZKTUSDRWVHOL/action/author_attestation","sign_citation":"https://pith.science/pith/7B5QVX7AB2ZGR7ZKTUSDRWVHOL/action/citation_signature","submit_replication":"https://pith.science/pith/7B5QVX7AB2ZGR7ZKTUSDRWVHOL/action/replication_record"}},"created_at":"2026-05-17T23:47:43.832873+00:00","updated_at":"2026-05-17T23:47:43.832873+00:00"}