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Paper Citation Record · LEDGER

PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.06378.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.06378 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:32:48.753605Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-07T23:26:44.472746Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cf856e30-c125-4698-a173-c4117320a65e · inbound

Learning a Neural Association Network for Self-supervised Multi-Object Tracking cites this paper.

Learning a Neural Association Network for Self-supervised Multi-Object Tracking PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-12T18:32:48.753605Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T18:32:48.753605Z digest=sha256:6d68b11012082e2c5c57c9cebcdd0904f98a90572ea81146cdc1c28544cbcaab

Observation 4aa2f84f-ce6d-4725-96d5-c7ae50b56050 · inbound

IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter cites this paper.

IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter PKF: Probabilistic Data Association Kalman Filter for Multi-Object Tracking

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-07T23:26:44.476737Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-07T23:26:44.372550Z digest=sha256:25f6c51d3a831da2f389bb2aeee7ad0a19b312025519d5938708101af3971315