Paper Citation Record · LEDGER

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection

As of 16 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2508.20447.

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

pith.paper-citation-record.v1
2508.20447 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T15:10:45.134961Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

43 of 43 outbound references displayed

verified exact0
verified fuzzy36
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 304f8784-8db2-45a6-849d-6a2f2ee6192b · outbound

This paper cites Enhancing multi-view pedestrian detection through generalized 3D feature pulling.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Enhancing multi-view pedestrian detection through generalized 3D feature pulling

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.533721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 14de330d-2be4-41c1-8d02-e37d3bd8302c · outbound

This paper cites Multi-view pedestrian occupancy prediction with a novel synthetic dataset.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Multi-view pedestrian occupancy prediction with a novel synthetic dataset

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.524559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7d7b12a8-30fc-4035-880e-9b822574dc65 · outbound

This paper cites Deep occlusion reasoning for multi- camera multi-target detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Deep occlusion reasoning for multi- camera multi-target detection

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.515443Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2ad3e40d-20f3-4140-aa7b-57271f60740f · outbound

This paper cites Deep multi-camera people detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Deep multi-camera people detection

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.506019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a41946aa-8fcb-4b29-91c2-384b8ccad994 · outbound

This paper cites Wildtrack: A multi-camera hd dataset for dense unscripted pedestrian detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Wildtrack: A multi-camera hd dataset for dense unscripted pedestrian detection

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.496177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation aaca3af7-9274-4217-ac62-f2a57d2e64af · outbound

This paper cites YOLO-MS: rethinking multi-scale representation learning for real-time object detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection YOLO-MS: rethinking multi-scale representation learning for real-time object detection

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.485901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3d9cca8b-ba91-4c34-b230-75177085da91 · outbound

This paper cites SportsMOT: A large multi-object tracking dataset in multiple sports scenes.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection SportsMOT: A large multi-object tracking dataset in multiple sports scenes

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.476797Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 639468c0-4e15-46ba-b306-ae0abdbc4106 · outbound

This paper cites Histograms of oriented gradients for human detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Histograms of oriented gradients for human detection

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.467845Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ebe7d23d-2326-429f-afea-d26c48ede86b · outbound

This paper cites ImageNet: A large-scale hierarchical image database.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection ImageNet: A large-scale hierarchical image database

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-05T15:10:45.024824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27c32624-cd3b-4d60-8240-7db51f9ec515 · outbound

This paper cites Pedestrian detection: An evaluation of the state of the art.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Pedestrian detection: An evaluation of the state of the art

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.453738Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c75fab12-0606-4d2c-8645-d2da53cb8db3 · outbound

This paper cites Multi-object detection and tracking (MODT) machine learning model for real-time video surveillance systems.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Multi-object detection and tracking (MODT) machine learning model for real-time video surveillance systems

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.445093Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7669b1c1-09d9-436c-b1fc-09e728c0a0bd · outbound

This paper cites Two-level data augmen- tation for calibrated multi-view detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Two-level data augmen- tation for calibrated multi-view detection

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.436059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 240ce4e4-0003-4c8f-8e0d-3180388d3341 · outbound

This paper cites Multicamera people tracking with a probabilistic occupancy map.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Multicamera people tracking with a probabilistic occupancy map

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.426771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ebc4419d-2df8-40f3-a9ca-08e2f9da7caf · outbound

This paper cites YOLOX: Exceeding YOLO Series in 2021.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection YOLOX: Exceeding YOLO Series in 2021

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T15:10:45.040454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc33b288-556d-45bb-bd77-9d6afff2160f · outbound

This paper cites Deep residual learning for image recognition.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Deep residual learning for image recognition

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T15:10:45.043875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6126aceb-8b30-4cb8-86d1-6961e183e924 · outbound

This paper cites Multiview detection with shadow transformer (and view-coherent data augmentation).

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Multiview detection with shadow transformer (and view-coherent data augmentation)

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.411903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ed0a15be-290a-4672-997e-3a807599a2d6 · outbound

This paper cites Multiview detection with feature perspective transformation.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Multiview detection with feature perspective transformation

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.403163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 090d9f33-c088-4c52-accd-c3125f6a6330 · outbound

This paper cites Booster-SHOT: Boosting stacked ho- mography transformations for multiview pedestrian detection with attention.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Booster-SHOT: Boosting stacked ho- mography transformations for multiview pedestrian detection with attention

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.393693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a6e85307-0e38-475b-b65c-9662e44d0af1 · outbound

This paper cites Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Framework for performance evaluation of face, text, and vehicle detection and tracking in video: Data, metrics, and protocol

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.383924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bd5ec0fb-213f-4dc3-8a99-63f254359897 · outbound

This paper cites F2DNet: Fast focal detection network for pedestrian detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection F2DNet: Fast focal detection network for pedestrian detection

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.374725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2243ba1a-2675-49c6-ade6-4c698dbb8dad · outbound

This paper cites Localized semantic feature mixers for efficient pedestrian detection in autonomous driving.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Localized semantic feature mixers for efficient pedestrian detection in autonomous driving

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.365739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5068fc4d-b307-4b12-96fb-f4f5ec996c73 · outbound

This paper cites Feature pyramid networks for object detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Feature pyramid networks for object detection

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.356724Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 3bbda6e4-e1e2-459b-9e30-83f21b71fd3a · outbound

This paper cites Focal loss for dense object detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Focal loss for dense object detection

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-05T15:10:45.069187Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4a711c4-e724-4525-bc05-84af004ae66f · outbound

This paper cites Path aggregation network for instance segmentation.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Path aggregation network for instance segmentation

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.342315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a677511c-e351-47c2-8c5d-beb7c047d42f · outbound

This paper cites SSD: Single shot multibox detector.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection SSD: Single shot multibox detector

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.333486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6a5b5a8e-756b-4339-8a1d-982dcf4fec3d · outbound

This paper cites Center and scale prediction: Anchor-free approach for pedestrian and face detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Center and scale prediction: Anchor-free approach for pedestrian and face detection

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.325075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2af22f20-9001-48d4-94eb-d2a8818b622d · outbound

This paper cites Loshchilov and F.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Loshchilov and F

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.316245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 86fe5aa0-6feb-449f-8e1a-11e66bf3e961 · outbound

This paper cites Distinctive image features from scale-invariant keypoints.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Distinctive image features from scale-invariant keypoints

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.307290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0cf3ad1a-3512-4610-9033-62ca40481c64 · outbound

This paper cites RTMDet: An Empirical Study of Designing Real-Time Object Detectors.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection RTMDet: An Empirical Study of Designing Real-Time Object Detectors

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-05T15:10:45.089133Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33491015-8ef2-4acb-a2f7-7f31b28e0d21 · outbound

This paper cites 3D random occlusion and multi-layer projection for deep multi-camera pedestrian localization.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection 3D random occlusion and multi-layer projection for deep multi-camera pedestrian localization

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.298058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fb17615c-96ef-43fa-8802-1c40ed0a237c · outbound

This paper cites Conditional random fields for multi-camera object detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Conditional random fields for multi-camera object detection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.288871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d106af3e-0500-489b-9cf3-099d24126ef9 · outbound

This paper cites Stacked homography transformations for multi-view pedestrian detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Stacked homography transformations for multi-view pedestrian detection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.280004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c4fd707d-bc33-4621-9222-528f73ae7ec0 · outbound

This paper cites Scene generalized multi-view pedestrian detection with rotation-based augmentation and regularization.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Scene generalized multi-view pedestrian detection with rotation-based augmentation and regularization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.270156Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f204a2b8-43f3-4b8c-b106-bb5cd9c9fa3f · outbound

This paper cites EfficientDet: Scalable and efficient object detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection EfficientDet: Scalable and efficient object detection

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.261025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e06b3833-d95c-4873-a353-5c6f9dbc29e1 · outbound

This paper cites Vaswani, N.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Vaswani, N

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-05T15:10:45.108523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd7bbc68-aaae-401e-814f-bddf63788c9b · outbound

This paper cites Bringing generalization to deep multi-view pedestrian detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Bringing generalization to deep multi-view pedestrian detection

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.246132Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 191e88aa-d11d-48e3-9315-66590988d47f · outbound

This paper cites A multi modal people tracker for real time human robot interaction.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection A multi modal people tracker for real time human robot interaction

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.237198Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ca0aa8e4-b4e2-41c0-adeb-bd2232a70924 · outbound

This paper cites Multi-view people tracking via hierarchical trajectory composition.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Multi-view people tracking via hierarchical trajectory composition

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.226884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation fb8abb55-f5ad-46a7-a0fd-643663f86528 · outbound

This paper cites Mahalanobis distance-based multi-view optimal transport for multi-view crowd localization.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Mahalanobis distance-based multi-view optimal transport for multi-view crowd localization

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.216798Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 8a659660-25a1-46c1-a97a-4b1b2911fc3d · outbound

This paper cites CityPersons: A diverse dataset for pedestrian detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection CityPersons: A diverse dataset for pedestrian detection

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.207931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cd3ffec8-c8a7-4afa-92af-a09aed72ee49 · outbound

This paper cites DETRs beat YOLOs on real-time object detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection DETRs beat YOLOs on real-time object detection

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.198007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7af8452a-af67-4b18-9397-4fd3ad0a96eb · outbound

This paper cites Objects as Points.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Objects as Points

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-05T15:10:45.131501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca1d2c5f-ce62-47b6-83f2-318e4edad79d · outbound

This paper cites Deformable DETR: Deformable transformers for end-to-end object detection.

MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection Deformable DETR: Deformable transformers for end-to-end object detection

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T15:10:45.188942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.