Paper Citation Record · LEDGER

ByteTrack: Multi-Object Tracking by Associating Every Detection Box

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2110.06864.

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

pith.paper-citation-record.v1
2110.06864 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:00:12.429721Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

106
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 8ddaa5fa-3700-4710-b86f-137bfbf5d94b · inbound

YOLOv8-SMOT: An Efficient and Robust Framework for Real-Time Small Object Tracking via Slice-Assisted Training and Adaptive Association cites this paper.

YOLOv8-SMOT: An Efficient and Robust Framework for Real-Time Small Object Tracking via Slice-Assisted Training and Adaptive Association ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T17:00:12.429721Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 38754eaf-d359-40f9-97f6-7d938eecb7de · inbound

Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction cites this paper.

Design Insights and Comparative Evaluation of a Hardware-Based Cooperative Perception Architecture for Lane Change Prediction ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T15:18:16.964746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdbb4810-3a6c-4336-aaab-56270569560b · inbound

Occlusion-Aware Multi-Object Tracking via Expected Probability of Detection cites this paper.

Occlusion-Aware Multi-Object Tracking via Expected Probability of Detection ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-17T04:59:04.004405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7972c041-bf74-45de-9a43-a71c5b5295c2 · inbound

CD-TWINSAFE: A ROS-enabled Digital Twin for Scene Understanding and Safety Emerging V2I Technology cites this paper.

CD-TWINSAFE: A ROS-enabled Digital Twin for Scene Understanding and Safety Emerging V2I Technology ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-21T15:10:16.540549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8983e44d-dc94-4883-a91d-8277bb553a1d · inbound

Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking cites this paper.

Time-series Meets Complex Motion Modeling: Robust and Computational-effective Motion Predictor for Multi-object Tracking ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-09T20:06:36.915579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c5a38dce-025a-4de7-a64b-90bf680a0b5f · inbound

Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations cites this paper.

Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-25T05:55:24.133146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 67affccb-c0ef-4f2c-9132-bdfc5a6b270f · inbound

Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations cites this paper.

Suicide Risk Assessment from AI-powered Video Surveillance: An Interpretable Framework for Prevention in Metro Stations ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:44:55.731164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 79dfed61-be6e-42fb-bd42-1863874c872d · inbound

Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI cites this paper.

Kairos: A Regret-Aware Native World-Action Model Stack for Physical AI ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-07-03T17:28:44.949263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 9b2b5ed8-0979-458a-8c5e-20a8c775fef2 · inbound

Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing cites this paper.

Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing ByteTrack: Multi-Object Tracking by Associating Every Detection Box

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-11T11:26:33.422369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.