Paper Citation Record · LEDGER

Dynamic Region Division for Adaptive Learning Pedestrian Counting

As of 16 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 1 inbound Pith citation observation for arXiv:1908.03978.

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

pith.paper-citation-record.v1
1908.03978 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:00:56.537026Z

measured 26 of 26 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T14:00:56.476743Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T14:00:56.563701Z

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy22
unresolved2
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7d5b0a73-dfc7-4b8a-b477-f2a2fb834c83 · outbound

This paper cites Dynamic Region Division for Adaptive Learning Pedestrian Counting.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Dynamic Region Division for Adaptive Learning Pedestrian Counting

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T14:00:56.568441Z

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 c6f18a59-50d5-479f-8afc-1351a52500d3 · outbound

This paper cites 2) Appropriate learn- ing models are applied to count pedestrians in each obtained region.

Dynamic Region Division for Adaptive Learning Pedestrian Counting 2) Appropriate learn- ing models are applied to count pedestrians in each obtained region

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.730959Z

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 d67bb662-cc34-44dd-a7f6-3a1c2e1c59c9 · outbound

This paper cites [2] and used 2D gaussian kernel to model one pedestrian.

Dynamic Region Division for Adaptive Learning Pedestrian Counting [2] and used 2D gaussian kernel to model one pedestrian

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.724157Z

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 1aa4801d-c1ba-4879-8553-615eff10c5f5 · outbound

This paper cites Overview To overcome the error of gaussian kernel simulation and misidentiﬁcation of clutter background caused by perspective distortion, a novel algorithm framwork is proposed.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Overview To overcome the error of gaussian kernel simulation and misidentiﬁcation of clutter background caused by perspective distortion, a novel algorithm framwork is proposed

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.716472Z

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 c1e86117-b37c-433f-9b03-e9739318840d · outbound

This paper cites Counting model for distant region Li et al.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Counting model for distant region Li et al

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.708266Z

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 bd8e6176-38d0-41cb-83f6-3b59b86baa80 · outbound

This paper cites Experiment dataset We evaluate the proposed algorithm through extensive exper- iments on the publicly available Subway station pedestrian dataset [7].

Dynamic Region Division for Adaptive Learning Pedestrian Counting Experiment dataset We evaluate the proposed algorithm through extensive exper- iments on the publicly available Subway station pedestrian dataset [7]

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.700237Z

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 e2282355-c8bd-4d23-b739-83456ac0b316 · outbound

This paper cites The novel dynamic region division can meet the challenge of perspective distortion and avoid to cut the head into two parts.

Dynamic Region Division for Adaptive Learning Pedestrian Counting The novel dynamic region division can meet the challenge of perspective distortion and avoid to cut the head into two parts

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.692190Z

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 d543ab57-6f03-4d60-aba6-4666a77c15cd · outbound

This paper cites Research on the impact of crowd ﬂow on crowd risk in large gathering spots,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Research on the impact of crowd ﬂow on crowd risk in large gathering spots,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.683865Z

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 40d18f47-9514-490a-b728-6e1534d758e5 · outbound

This paper cites Learning to count objects in images,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Learning to count objects in images,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.675891Z

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 38889fab-3a8f-423b-b9ce-62fd9cd4c561 · outbound

This paper cites Learning a perspective- embedded deconvolution network for crowd counting,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Learning a perspective- embedded deconvolution network for crowd counting,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.668949Z

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 542b342a-22a5-4f3d-922d-e71afc47006e · outbound

This paper cites Histograms of ori- ented gradients for human detection,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Histograms of ori- ented gradients for human detection,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-14T14:00:56.501144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ca30378-6edb-4105-abd5-90e8789683e4 · outbound

This paper cites You only look once: Uniﬁed, real-time object detection,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting You only look once: Uniﬁed, real-time object detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.658135Z

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 911ef531-21dc-4fdf-b82a-3aa3c071dfbf · outbound

This paper cites Learning to count with regression forest and structured labels,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Learning to count with regression forest and structured labels,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.651757Z

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 29a38e67-d0b9-498f-9c25-305e3b58eaa3 · outbound

This paper cites A double-region learning algorithm for counting the number of pedestrians in subway surveil- lance videos,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting A double-region learning algorithm for counting the number of pedestrians in subway surveil- lance videos,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.645447Z

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 7261ba8f-2504-4cc7-a38c-5a52f4234a0f · outbound

This paper cites Cross-scene crowd counting via deep convolutional neural networks,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Cross-scene crowd counting via deep convolutional neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.639383Z

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 5b4f62ed-3f43-423a-a5aa-b1a578d60339 · outbound

This paper cites Single-image crowd counting via multi-column convolutional neural network,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Single-image crowd counting via multi-column convolutional neural network,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.632070Z

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 687b81d9-072f-42e8-ab17-1faae55916fa · outbound

This paper cites Body structure aware deep crowd counting,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Body structure aware deep crowd counting,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.624889Z

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 ecfb8573-5a70-42b1-96f0-5d891ea8e296 · outbound

This paper cites Csr- net: Dilated convolutional neural networks for under- standing the highly congested scenes,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Csr- net: Dilated convolutional neural networks for under- standing the highly congested scenes,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.617510Z

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 10a1b838-91e4-408c-b2b1-55cf20cd882d · outbound

This paper cites Divide and grow: Captur- ing huge diversity in crowd images with incrementally growing cnn,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Divide and grow: Captur- ing huge diversity in crowd images with incrementally growing cnn,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.609863Z

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 eb1cc335-4b0a-44a2-baa7-7a0cc12cf719 · outbound

This paper cites Cnn-based cascaded multi-task learning of high-level prior and den- sity estimation for crowd counting,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Cnn-based cascaded multi-task learning of high-level prior and den- sity estimation for crowd counting,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.602136Z

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 5a4019b6-0c5b-4a51-a09e-a82ef2bb2bdb · outbound

This paper cites Rich feature hierarchies for accurate object detection and semantic segmentation,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Rich feature hierarchies for accurate object detection and semantic segmentation,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.595234Z

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 503b0434-f9df-404e-9807-b27d28701675 · outbound

This paper cites Ssd: Single shot multibox detector,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Ssd: Single shot multibox detector,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.588506Z

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 76710414-c68f-4adc-b121-94bb5f85bdcb · outbound

This paper cites YOLOv3: An Incremental Improvement.

Dynamic Region Division for Adaptive Learning Pedestrian Counting YOLOv3: An Incremental Improvement

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-14T14:00:56.531914Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54de3a57-dc5c-46c4-8f77-d68bbea78544 · outbound

This paper cites Going deeper with convolutions,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Going deeper with convolutions,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.581952Z

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 1047a66a-ff12-4ae3-b589-d94ea49fd7c3 · outbound

This paper cites Microsoft coco: Common objects in context,.

Dynamic Region Division for Adaptive Learning Pedestrian Counting Microsoft coco: Common objects in context,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T14:00:56.575259Z

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

Observation 7d5b0a73-dfc7-4b8a-b477-f2a2fb834c83 · inbound