Paper Citation Record · LEDGER

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images

As of 19 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 2 inbound Pith citation observations for arXiv:2502.04014.

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

pith.paper-citation-record.v1
2502.04014 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:55:09.359231Z

measured 57 of 57 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:38:23.655493Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T23:02:46.003582Z

Reference resolution

55 of 55 outbound references displayed

verified exact1
verified fuzzy46
unresolved8
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fc539bd5-9adb-4180-a0ae-dc7de5ede560 · outbound

This paper cites Applications of unmanned aerial vehicle ( UAV ) in road safety, traffic and highway infrastructure management: Recent advances and challenges.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Applications of unmanned aerial vehicle ( UAV ) in road safety, traffic and highway infrastructure management: Recent advances and challenges

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.921822Z

Source-reported events for the cited work

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Observation e495b1cd-27c7-4306-b566-ce5b2af623e1 · outbound

This paper cites Unmanned aerial vehicles as element of road traffic safety monitoring.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Unmanned aerial vehicles as element of road traffic safety monitoring

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.912375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 1bc159e0-049c-4b01-be63-4a7ae8c7a824 · outbound

This paper cites Urban traffic monitoring and analysis using unmanned aerial vehicles ( UAVs ): A systematic literature review.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Urban traffic monitoring and analysis using unmanned aerial vehicles ( UAVs ): A systematic literature review

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.902201Z

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Observation 18bc4c85-ae70-4c8b-9636-0b4fc051ef84 · outbound

This paper cites Unmanned aerial vehicles applications in future smart cities.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Unmanned aerial vehicles applications in future smart cities

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.892597Z

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Observation c68de753-7a32-4902-b09b-85e148e7e7da · outbound

This paper cites UAV fleet as a dependable service for smart cities: Model-based assessment and application.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images UAV fleet as a dependable service for smart cities: Model-based assessment and application

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.883108Z

Source-reported events for the cited work

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Observation 33350524-0d87-4196-bc3a-fb8859a8faa0 · outbound

This paper cites Object detection in optical remote sensing images: A survey and a new benchmark.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Object detection in optical remote sensing images: A survey and a new benchmark

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.873589Z

Source-reported events for the cited work

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Observation ea2c6cc3-d080-44c8-afd1-9abc184b4bcd · outbound

This paper cites Fair1m: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Fair1m: A benchmark dataset for fine-grained object recognition in high-resolution remote sensing imagery

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.864575Z

Source-reported events for the cited work

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Observation a5ef4003-eb8c-4c53-894a-032d0d5c65f6 · outbound

This paper cites Deep learning for small and tiny object detection: A survey.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Deep learning for small and tiny object detection: A survey

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.855040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d84bf1f8-91e0-4fb0-a498-9084e1c9ead2 · outbound

This paper cites Unmanned aerial vehicles for crowd monitoring and analysis.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Unmanned aerial vehicles for crowd monitoring and analysis

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.844998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9ec79229-8353-4160-9bda-6aa25e76aaf0 · outbound

This paper cites Unmanned aerial vehicle communications for civil applications: A review.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Unmanned aerial vehicle communications for civil applications: A review

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.835442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7bfa32bc-741e-4414-9192-7a4cfa70db63 · outbound

This paper cites Assistance of uavs in the intelligent management of urban space: A survey.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Assistance of uavs in the intelligent management of urban space: A survey

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.825573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13b86b10-3380-4cd1-8b14-8c9d0a17f09d · outbound

This paper cites UAV -based IoT platform: A crowd surveillance use case.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images UAV -based IoT platform: A crowd surveillance use case

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.816490Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cd61ec6f-5f1a-4c94-bdd5-ba31ebaef24e · outbound

This paper cites Development of automated people counting system using object detection and tracking.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Development of automated people counting system using object detection and tracking

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.807530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 58a84aa6-0816-4cc1-92ea-ed4163335a68 · outbound

This paper cites A survey of object detection for uavs based on deep learning.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images A survey of object detection for uavs based on deep learning

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.797779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 39309943-822a-4463-a05e-eadd4ee8e0ed · outbound

This paper cites Efficient high-resolution deep learning: A survey.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Efficient high-resolution deep learning: A survey

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.788641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9704c3cf-b6d6-41e7-9715-721be53fbd67 · outbound

This paper cites On-board crowd counting and density estimation using low altitude unmanned aerial vehicles—looking beyond beating the benchmark.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images On-board crowd counting and density estimation using low altitude unmanned aerial vehicles—looking beyond beating the benchmark

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.779228Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 6902cd5d-741b-4e4f-a963-8766339375b1 · outbound

This paper cites An empirical study of context in object detection.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images An empirical study of context in object detection

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.769461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dff71fd6-2303-4194-8e67-2d3729bbd60a · outbound

This paper cites Oriented ship detection based on soft thresholding and context information in SAR images of complex scenes.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Oriented ship detection based on soft thresholding and context information in SAR images of complex scenes

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.760423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 842ef275-4f4e-4ff7-b898-a643d8490f58 · outbound

This paper cites Detection, tracking, and counting meets drones in crowds: A benchmark.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Detection, tracking, and counting meets drones in crowds: A benchmark

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.751326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 24632064-f150-4897-adad-a4b4ca31f759 · outbound

This paper cites Context-aware crowd counting.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Context-aware crowd counting

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.742625Z

Source-reported events for the cited work

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Observation ef885c66-d839-4391-af4a-58854e39366b · outbound

This paper cites CSRNet : Dilated convolutional neural networks for understanding the highly congested scenes.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images CSRNet : Dilated convolutional neural networks for understanding the highly congested scenes

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.733603Z

Source-reported events for the cited work

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Observation 1c191fc5-9c66-4487-8fd6-6d66603748c9 · outbound

This paper cites Distribution matching for crowd counting.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Distribution matching for crowd counting

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.724376Z

Source-reported events for the cited work

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Observation 12128c57-f1b4-43d8-b253-dc7017e14f7f · outbound

This paper cites Rethinking counting and localization in crowds: A purely point-based framework.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Rethinking counting and localization in crowds: A purely point-based framework

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.715055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 10086c3e-48d5-46c3-93cd-c02eacd27193 · outbound

This paper cites An end-to-end transformer model for crowd localization.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images An end-to-end transformer model for crowd localization

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.705720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8cd9ced5-78b4-4e08-b13a-3584bae0584f · outbound

This paper cites Boosting detection in crowd analysis via underutilized output features.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Boosting detection in crowd analysis via underutilized output features

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.696171Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2a7e89af-62a2-404f-be84-a8104461df1c · outbound

This paper cites STEERER : Resolving scale variations for counting and localization via selective inheritance learning.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images STEERER : Resolving scale variations for counting and localization via selective inheritance learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.686851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d53829b-e4c4-4f88-b0c0-46a8e41b86b1 · outbound

This paper cites Multi-frame attention with feature-level warping for drone crowd tracking.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Multi-frame attention with feature-level warping for drone crowd tracking

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.677189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9c6d23c1-92ab-42c3-a4cf-dbffe811c88a · outbound

This paper cites U-net : Convolutional networks for biomedical image segmentation.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images U-net : Convolutional networks for biomedical image segmentation

Reference 28

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Source-reported events for the cited work

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Observation d1846de2-8a2a-40a2-89ce-e0ae32f64f62 · outbound

This paper cites Tiny object detection in aerial images.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Tiny object detection in aerial images

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.658059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3fdcaf5b-0b99-48cd-9f61-d9a3053936c4 · outbound

This paper cites A Normalized Gaussian Wasserstein Distance for Tiny Object Detection.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images A Normalized Gaussian Wasserstein Distance for Tiny Object Detection

Reference 30

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Source-reported events for the cited work

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Observation 5fbe248d-0408-4d7c-88d3-f367bd10956a · outbound

This paper cites RFLA : Gaussian receptive field based label assignment for tiny object detection.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images RFLA : Gaussian receptive field based label assignment for tiny object detection

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.648551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fa557b3c-c097-4051-978b-60b29037b039 · outbound

This paper cites A transformer-based framework for tiny object detection.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images A transformer-based framework for tiny object detection

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.639290Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2193a56b-da8c-49ee-ab2d-e0d80cf3d14c · outbound

This paper cites Focal loss for dense object detection.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Focal loss for dense object detection

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.629886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 235a5198-d9ae-4dce-91b4-146a66dba933 · outbound

This paper cites Focal inverse distance transform maps for crowd localization.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Focal inverse distance transform maps for crowd localization

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.620679Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 61e7e29d-1536-4d17-8174-51e96d90c46f · outbound

This paper cites Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Mask Focal Loss: A unifying framework for dense crowd counting with canonical object detection networks

Reference 35

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Source-reported events for the cited work

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Observation 589ee083-c176-41b1-b873-260ff2260f76 · outbound

This paper cites Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.611365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 16aa0c04-90d7-4676-8f91-b7ab977f8149 · outbound

This paper cites Coordinate attention for efficient mobile network design.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Coordinate attention for efficient mobile network design

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.601324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7738bf08-8538-414c-8b0e-5bce83fbffaf · outbound

This paper cites What makes ImageNet good for transfer learning?.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images What makes ImageNet good for transfer learning?

Reference 38

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Source-reported events for the cited work

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This paper cites Do we still need ImageNet pre-training in remote sensing scene classification?.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Do we still need ImageNet pre-training in remote sensing scene classification?

Reference 39

Resolution: verified exact; local_arxiv, observed 2026-08-08T23:55:09.450456Z

Source-reported events for the cited work

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Observation 2fc0a19f-452d-4fd9-a48b-749d2f7e0f82 · outbound

This paper cites Swin transformer embedding unet for remote sensing image semantic segmentation.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Swin transformer embedding unet for remote sensing image semantic segmentation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.592480Z

Source-reported events for the cited work

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Observation 10c162bf-df91-4d45-bfbb-9747ff0a92f4 · outbound

This paper cites SegFormer : Simple and efficient design for semantic segmentation with transformers.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images SegFormer : Simple and efficient design for semantic segmentation with transformers

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.582801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 8d54b38f-c4a5-4152-b0df-cc816ce340b2 · outbound

This paper cites Cornernet: Detecting objects as paired keypoints.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Cornernet: Detecting objects as paired keypoints

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.572958Z

Source-reported events for the cited work

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Observation a95db1c7-ddbb-4f54-9f67-649dd0257141 · outbound

This paper cites YOLOv7 : Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images YOLOv7 : Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.563427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a804bf2e-d92b-41af-afaa-f138f2132cef · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 44

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Source-reported events for the cited work

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Observation cdc882e7-57af-4c62-b257-9d4ed8685715 · outbound

This paper cites Decoupled Weight Decay Regularization.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Decoupled Weight Decay Regularization

Reference 45

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Source-reported events for the cited work

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Observation f4301f24-68c0-4c03-89e4-faa6489e8881 · outbound

This paper cites Attention mechanisms in computer vision: A survey.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Attention mechanisms in computer vision: A survey

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.553857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9c8280eb-1b53-4f12-96e5-b8221e63ed7a · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-08T23:55:09.331970Z

Source-reported events for the cited work

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Observation bca27afb-9a14-47e0-b0c8-18b0bb8e02ae · outbound

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

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Imagenet: A large-scale hierarchical image database

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.543731Z

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Observation 01d2c1a3-73a9-4a6a-9672-995dcc1da2cc · outbound

This paper cites Training Deeper Convolutional Networks with Deep Supervision.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Training Deeper Convolutional Networks with Deep Supervision

Reference 49

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Observation e756fdfe-9167-4522-ac5a-657bf539fb37 · outbound

This paper cites Object detection in aerial images: A large-scale benchmark and challenges.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Object detection in aerial images: A large-scale benchmark and challenges

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.533355Z

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Observation 3bcadd42-2673-4e09-9d6b-7a17e3aab0ff · outbound

This paper cites Towards large-scale small object detection: Survey and benchmarks.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Towards large-scale small object detection: Survey and benchmarks

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.523611Z

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Observation 5061ecb5-9850-4815-a910-f3859caa01d8 · outbound

This paper cites The unmanned aerial vehicle benchmark: Object detection and tracking.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images The unmanned aerial vehicle benchmark: Object detection and tracking

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.513386Z

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Observation 7991c727-dbdf-4116-84e0-d18b66b4ea46 · outbound

This paper cites Deep residual learning for image recognition.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Deep residual learning for image recognition

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.503103Z

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Observation 443bf1db-3140-4235-ba30-b077cfc2c4a9 · outbound

This paper cites ResNet strikes back: An improved training procedure in timm.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images ResNet strikes back: An improved training procedure in timm

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T23:55:09.355777Z

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Observation f36ac5c1-ec4f-4623-8eac-fd44ab34c1d6 · outbound

This paper cites Efficientnet: Rethinking model scaling for convolutional neural networks.

Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images Efficientnet: Rethinking model scaling for convolutional neural networks

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T23:55:09.492559Z

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Pith citing papers

Observation 7a6b8d66-68ab-45a9-be69-78c0a528012f · inbound

Improving trajectory continuity in drone-based crowd monitoring using a set of minimal-cost techniques and deep discriminative correlation filters cites this paper.

Improving trajectory continuity in drone-based crowd monitoring using a set of minimal-cost techniques and deep discriminative correlation filters Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T05:38:23.655493Z

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Observation a93b901a-c493-43a8-b504-50a7ca2b85df · inbound

Count Anything cites this paper.

Count Anything Enhancing people localisation in drone imagery for better crowd management by utilising every pixel in high-resolution images

Reference 83

Resolution: verified exact; arxiv_id, observed 2026-06-28T23:02:46.005364Z

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