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Paper Citation Record · LEDGER

MOT20: A benchmark for multi object tracking in crowded scenes

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 48 inbound Pith citation observations for arXiv:2003.09003.

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

pith.paper-citation-record.v1
2003.09003 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 48 of 48 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 48 of 48 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T17:04:42.817394Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-10T02:26:43.122521Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

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Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 78c96587-ef89-4853-8e4d-046b5da21b1f · inbound

Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity cites this paper.

Enhancing Thermal MOT: A Novel Box Association Method Leveraging Thermal Identity and Motion Similarity MOT20: A benchmark for multi object tracking in crowded scenes

Reference 12

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source=pdf_text observed=2026-08-12T17:04:42.817394Z digest=sha256:b8b32ebc2be5b5dafc226c0fc659e69c7e6b3cb00df3bdea6bad08986fc29253

Observation df790f99-ebc2-49c5-8200-16c1f18bc9ec · inbound

Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos cites this paper.

Heterogeneous Graph Transformer for Multiple Tiny Object Tracking in RGB-T Videos MOT20: A benchmark for multi object tracking in crowded scenes

Reference 58

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source=pdf_text observed=2026-08-11T15:35:59.530532Z digest=sha256:c19ef66577b3e7b92d6eee8071d419b02c18aa29535cc1778855457bb449a743

Observation 0f0acaf8-4fd4-497b-9132-6a6ee62095cd · inbound

Mining Platoon Patterns from Traffic Videos cites this paper.

Mining Platoon Patterns from Traffic Videos MOT20: A benchmark for multi object tracking in crowded scenes

Reference 5

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source=pdf_text observed=2026-08-10T23:38:51.098457Z digest=sha256:0daf2045d776a7541680d9ddadfa56a6d697eaf67bccde5765977cb4ed52dbdc

Observation c1905424-0e7a-4912-ba0a-79311510bd7d · inbound

FusionSORT: Fusion Methods for Online Multi-object Visual Tracking cites this paper.

FusionSORT: Fusion Methods for Online Multi-object Visual Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 33

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source=pdf_text observed=2026-08-10T22:43:54.165308Z digest=sha256:5504af70c6e45a0e8114f9d1a4535fe5170d6aaf0f0e3aaac51f846c180416ef

Observation fcef04ff-6299-4e55-8441-479a416bb16a · inbound

PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues cites this paper.

PD-SORT: Occlusion-Robust Multi-Object Tracking Using Pseudo-Depth Cues MOT20: A benchmark for multi object tracking in crowded scenes

Reference 43

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source=pdf_text observed=2026-08-10T18:32:49.413488Z digest=sha256:7d4cea044dbf3da442bda4e2b704a0d8d8b96eab425a74d435112f9dfe1eb488

Observation f8c9c3b3-4052-4242-8da4-6fcbfc026bee · inbound

Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection cites this paper.

Learning better representations for crowded pedestrians in offboard LiDAR-camera 3D tracking-by-detection MOT20: A benchmark for multi object tracking in crowded scenes

Reference 38

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source=pdf_text observed=2026-08-07T15:12:02.755723Z digest=sha256:9c575e04404404d57c5b27e1472a4e58e6d380f28931e549a8af2f75330da1e7

Observation 20baaf9f-998f-42f7-9a72-8e3fd6ab1342 · inbound

A Framework for Multi-View Multiple Object Tracking using Single-View Multi-Object Trackers on Fish Data cites this paper.

A Framework for Multi-View Multiple Object Tracking using Single-View Multi-Object Trackers on Fish Data MOT20: A benchmark for multi object tracking in crowded scenes

Reference 5

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source=pdf_text observed=2026-08-07T14:54:08.030421Z digest=sha256:98568176c358ad58ada204419cc11b251b9e48a2f58a715d82c0a0710f27a1b8

Observation fb07304c-61c7-4a05-81e3-b8440489d16c · inbound

Progressive Scaling Visual Object Tracking cites this paper.

Progressive Scaling Visual Object Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 23

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source=pdf_text observed=2026-08-07T14:11:07.390614Z digest=sha256:a1d04e6c1053baa5665fd4423806975cf0977a73ae7bd4a93cd87d55f16ec3bc

Observation d8249d2f-734a-4720-a8d1-da915aac5fc9 · inbound

LazyVLM: Neuro-Symbolic Approach to Video Analytics cites this paper.

LazyVLM: Neuro-Symbolic Approach to Video Analytics MOT20: A benchmark for multi object tracking in crowded scenes

Reference 4

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source=pdf_text observed=2026-08-07T13:30:18.850135Z digest=sha256:18f20549d8a2cb08959d08bb316ddf9198bb1a39ba0950ae9bd0eebcd0e53f3c

Observation 00d96be6-7ac8-43f4-8ed3-c4af5e657a83 · inbound

A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects cites this paper.

A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects MOT20: A benchmark for multi object tracking in crowded scenes

Reference 160

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source=pdf_text observed=2026-08-07T00:34:19.794333Z digest=sha256:efb912eed8d774b47475ea0780e6e055b9b95578acda334b29720f8391c0b9fb

Observation 6dcb88c6-924a-48c3-b675-a483529e51e9 · inbound

USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways cites this paper.

USVTrack: USV-Based 4D Radar-Camera Tracking Dataset for Autonomous Driving in Inland Waterways MOT20: A benchmark for multi object tracking in crowded scenes

Reference 40

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source=pdf_text observed=2026-08-06T23:21:37.820359Z digest=sha256:be55a023571f579f895dfb557e3eb1e60738b70210d4f2d04a20ad2e7750fed0

Observation f1d5293f-5743-4a0e-920f-298dd94fc396 · inbound

Trajectory Prediction in Dynamic Object Tracking: A Critical Study cites this paper.

Trajectory Prediction in Dynamic Object Tracking: A Critical Study MOT20: A benchmark for multi object tracking in crowded scenes

Reference 97

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source=pdf_text observed=2026-08-06T23:09:55.935547Z digest=sha256:775afcb92164bc861fb6001bd9839bd393250b1c44f06a08b8fda04e4444a388

Observation f543736c-604c-4501-845b-1d477d804e65 · inbound

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos cites this paper.

Lightweight Multi-Frame Integration for Robust YOLO Object Detection in Videos MOT20: A benchmark for multi object tracking in crowded scenes

Reference 7

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source=pdf_text observed=2026-08-06T22:50:41.211905Z digest=sha256:051bb4abf7db90a3eb04563349a5c4f8fe0f61467beace9f258b2876019c79ae

Observation d056222b-f4b1-401b-a141-7a3df1373026 · inbound

CrowdTrack: A Benchmark for Difficult Multiple Pedestrian Tracking in Real Scenarios cites this paper.

CrowdTrack: A Benchmark for Difficult Multiple Pedestrian Tracking in Real Scenarios MOT20: A benchmark for multi object tracking in crowded scenes

Reference 18

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source=pdf_text observed=2026-08-06T20:31:50.774169Z digest=sha256:3952d8fa1cb534bb5a204827b05e75aa23e23d7bbdda4ac8c98c8f1d286ab2a3

Observation a298dc06-3b75-457c-a5b6-cc3c1720b2a4 · inbound

RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking cites this paper.

RoundaboutHD: High-Resolution Real-World Urban Environment Benchmark for Multi-Camera Vehicle Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 7

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source=pdf_text observed=2026-08-06T18:18:02.216704Z digest=sha256:cf5adcfc54577f64dfd485b45c11936301fcfaf067063954ea034a7853583859

Observation 0451addd-cc52-4228-bb51-7b5494cf2e58 · inbound

Glance-MCMT: A General MCMT Framework with Glance Initialization and Progressive Association cites this paper.

Glance-MCMT: A General MCMT Framework with Glance Initialization and Progressive Association MOT20: A benchmark for multi object tracking in crowded scenes

Reference 5

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source=pdf_text observed=2026-08-06T17:41:38.137214Z digest=sha256:b6fa9bf9879adab8e7c9b23bbc95555c29f376586c3be51a88a4fdfa8bc2bc56

Observation ac126721-9701-4124-a187-95a809c31ceb · 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 MOT20: A benchmark for multi object tracking in crowded scenes

Reference 4

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source=pdf_text observed=2026-08-06T17:00:10.578887Z digest=sha256:7c6cdad4e848e6b06b7b527ae12ac5096ce0ffa818f911ea957f34f58a75dc78

Observation 0040d4bd-97a0-43f2-9d53-076c7ad01054 · inbound

Head Anchor Enhanced Detection and Association for Crowded Pedestrian Tracking cites this paper.

Head Anchor Enhanced Detection and Association for Crowded Pedestrian Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 24

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source=pdf_text observed=2026-08-05T23:22:52.773756Z digest=sha256:62b5ac09030cb2d007b038bb6148e887cc08d2877e41cbf4bec5f96f65e2c54f

Observation 4aabd58a-2663-4c2b-aaaa-3da97da32a78 · inbound

GRASPTrack: Geometry-Reasoned Association via Segmentation and Projection for Multi-Object Tracking cites this paper.

GRASPTrack: Geometry-Reasoned Association via Segmentation and Projection for Multi-Object Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 11

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source=pdf_text observed=2026-08-05T21:43:19.718350Z digest=sha256:19c57d08f171f7259f3e4d506c2a7eca54d6ca91027670336c2839c309a11869

Observation b73fd7f2-c500-46e4-98b8-b8d0ca54f2f4 · inbound

MeMoSORT: Memory-Assisted Filtering and Motion-Adaptive Association Metric for Multi-Person Tracking cites this paper.

MeMoSORT: Memory-Assisted Filtering and Motion-Adaptive Association Metric for Multi-Person Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 2023

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source=pdf_text observed=2026-08-05T20:54:15.967786Z digest=sha256:7a129c11373cd53bd50d193bf9d74647dee961529feb378aa382946487d05c82

Observation b8a4687f-a759-47c1-8e05-496ca5e96ba3 · inbound

To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software cites this paper.

To New Beginnings: A Survey of Unified Perception in Autonomous Vehicle Software MOT20: A benchmark for multi object tracking in crowded scenes

Reference 101

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source=pdf_text observed=2026-08-05T14:47:24.526609Z digest=sha256:7dddd76f94d2a098a6d8ebd04d3e922bf1e995952494e1a8df849b94920a6aa5

Observation 9f1a7084-befe-45fb-8fc9-e8b6cda382dd · inbound

MVTrajecter: Multi-View Pedestrian Tracking with Trajectory Motion Cost and Trajectory Appearance Cost cites this paper.

MVTrajecter: Multi-View Pedestrian Tracking with Trajectory Motion Cost and Trajectory Appearance Cost MOT20: A benchmark for multi object tracking in crowded scenes

Reference 15

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source=pdf_text observed=2026-08-05T12:55:35.836178Z digest=sha256:c50ff5e12eb00d59c7d959a256325cad43e1b3429248ef465b435d6467f39e19

Observation b02e73ae-4de6-4df9-b371-84985cb87484 · inbound

NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking cites this paper.

NOOUGAT: Towards Unified Online and Offline Multi-Object Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 76

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arxiv_id, observed 2026-05-18T20:01:50.295049Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T19:58:16.419838Z digest=sha256:89aaae62d738511faa077af3730fae8ef1f0cfc617127cad4ab95cf3769dce11

Observation 1915425c-0e43-4d30-adbe-a0d6b54375d9 · inbound

Motion Estimation for Multi-Object Tracking using KalmanNet with Semantic-Independent Encoding cites this paper.

Motion Estimation for Multi-Object Tracking using KalmanNet with Semantic-Independent Encoding MOT20: A benchmark for multi object tracking in crowded scenes

Reference 22

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source=pdf_text observed=2026-08-04T16:52:51.617862Z digest=sha256:b8252be4a974a2c7f281d3540ac098f5450b72c0cdad2dfaa5f64e24e47bf919

Observation ba0b9bd8-f6b7-4146-9738-a4f5da2fe885 · inbound

A Multi-purpose Tracking Framework for Salmon Welfare Monitoring in Challenging Environments cites this paper.

A Multi-purpose Tracking Framework for Salmon Welfare Monitoring in Challenging Environments MOT20: A benchmark for multi object tracking in crowded scenes

Reference 3

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arxiv_id, observed 2026-05-21T21:10:38.549482Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-21T21:09:32.938640Z digest=sha256:b531063e78b69930fb710c886e83a0e2f59bb9c213d6ab829c70bfc966157a9b

Observation 35961d89-d3aa-4052-ae41-7d602f0d145d · inbound

SVAG-Bench: A Large-Scale Benchmark for Multi-Instance Spatio-temporal Video Action Grounding cites this paper.

SVAG-Bench: A Large-Scale Benchmark for Multi-Instance Spatio-temporal Video Action Grounding MOT20: A benchmark for multi object tracking in crowded scenes

Reference 5

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arxiv_id, observed 2026-05-18T07:11:04.155735Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T07:06:06.322273Z digest=sha256:ee512fbaf6db6ea6398a09a5ba158e510f823471f722b44c5a57878a8810c0b4

Observation c510ca72-6c42-49e6-ad4d-66d9e912587f · inbound

OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback cites this paper.

OmniTrack++: Omnidirectional Multi-Object Tracking by Learning Large-FoV Trajectory Feedback MOT20: A benchmark for multi object tracking in crowded scenes

Reference 35

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arxiv_id, observed 2026-05-18T01:55:37.904178Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-18T01:55:04.637365Z digest=sha256:6231aa508dc7d2361c6d0e2caefe4265df696b82a308c09bfc8fe282dd990a3f

Observation f949bc42-a3e6-4487-aebc-046f417a075a · inbound

Edge Assisted Multi-Camera Vehicle Tracking Framework for Real-Time and Scalable Deployment cites this paper.

Edge Assisted Multi-Camera Vehicle Tracking Framework for Real-Time and Scalable Deployment MOT20: A benchmark for multi object tracking in crowded scenes

Reference 5

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arxiv_id, observed 2026-05-25T07:45:28.852981Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-25T07:44:25.569277Z digest=sha256:c86a4b0c5c810628d9ace8c8ae86d4fc5b9fde25a076e86f89c6222ebb5385af

Observation 48fe9b78-7f42-4cc2-8790-2d523cab8c69 · inbound

SatSAM2: Motion-Constrained Video Object Tracking in Satellite Imagery using Promptable SAM2 and Kalman Priors cites this paper.

SatSAM2: Motion-Constrained Video Object Tracking in Satellite Imagery using Promptable SAM2 and Kalman Priors MOT20: A benchmark for multi object tracking in crowded scenes

Reference 7

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arxiv_id, observed 2026-05-17T06:11:34.852478Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-17T06:10:24.636998Z digest=sha256:3acd0bb5736249089296e1fd5fc31d770c556687c31e1acb55891d6f34a2f1c9

Observation d88fa1f3-b20c-4bf1-90ae-281804057320 · inbound

Generative Semantic Multi-Object Tracking: A Large-Scale Benchmark and an MLLM-Driven Reasoning Framework cites this paper.

Generative Semantic Multi-Object Tracking: A Large-Scale Benchmark and an MLLM-Driven Reasoning Framework MOT20: A benchmark for multi object tracking in crowded scenes

Reference 8

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source=pdf_text observed=2026-08-03T11:28:05.823046Z digest=sha256:825388f1ce2e78c87ce453ea5af695fe5910cb0578cc029342684d8f4cf9750b

Observation 24b64445-b4df-4e81-beb0-1922616e3842 · inbound

Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding cites this paper.

Molmo2: Open Weights and Data for Vision-Language Models with Video Understanding and Grounding MOT20: A benchmark for multi object tracking in crowded scenes

Reference 30

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arxiv_id, observed 2026-05-16T04:21:29.717993Z

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No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-16T04:21:29.526008Z digest=sha256:56d2b7ab1765aae4ba4b9f04b7ab5d4429980e03169ca9b75d17f2429457d400

Observation dda19067-8d15-4b60-8e29-2c63d882edc6 · inbound

Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods cites this paper.

Video Individual Counting and Tracking from Moving Drones: A Benchmark and Methods MOT20: A benchmark for multi object tracking in crowded scenes

Reference 22

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source=pdf_text observed=2026-08-03T09:48:48.851639Z digest=sha256:8a0b134d153983ff36dea482d3922518025e376ff6ce0f77eb39e1e363639d53

Observation 624cd9a6-95fc-4e81-8db0-4f4268d79bc9 · inbound

COVTrack++: Learning Open-Vocabulary Multi-Object Tracking from Continuous Videos via a Synergistic Paradigm cites this paper.

COVTrack++: Learning Open-Vocabulary Multi-Object Tracking from Continuous Videos via a Synergistic Paradigm MOT20: A benchmark for multi object tracking in crowded scenes

Reference 1

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no resolver link, observed 2026-07-13T19:08:17.594352Z

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source=pdf_text observed=2026-07-13T19:08:17.594352Z digest=sha256:5e5a1f0847019ef06d2cb91966f1acb333ef0bc9c8c702dca7ea3ce56cd90d13

Observation 964a53e6-3ad2-4638-a55b-9168a1d30983 · inbound

Hypergraph-State Collaborative Reasoning for Multi-Object Tracking cites this paper.

Hypergraph-State Collaborative Reasoning for Multi-Object Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 10

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verified exact
arxiv_id, observed 2026-05-11T10:01:02.111339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-10T15:41:58.538994Z digest=sha256:81e07b1cd34906df41e9b5453fd451519af8cebd8a791f652c596373a6d0b595

Observation b727d18b-a1f8-40d5-883c-cb443f171a49 · inbound

Attention Is not Everything: Efficient Alternatives for Vision cites this paper.

Attention Is not Everything: Efficient Alternatives for Vision MOT20: A benchmark for multi object tracking in crowded scenes

Reference 89

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arxiv_id, observed 2026-05-10T05:51:10.433535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-10T05:46:33.053449Z digest=sha256:8527ee4ee2bb09acbfc76996544afa989562cb7afb4fc0bac7d3f78dd447cf43

Observation 45fc9a99-e827-4485-a64d-b8731901c44a · inbound

GateMOT: Q-Gated Attention for Dense Object Tracking cites this paper.

GateMOT: Q-Gated Attention for Dense Object Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 19

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T08:46:25.061518Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-07T13:57:52.724423Z digest=sha256:b43246db4aedb6b5ce71268b0afa2cc385201eca71a86bc9845f50ee99212132

Observation eee3df68-5ac6-4347-8eef-7dea75bd47a0 · 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 MOT20: A benchmark for multi object tracking in crowded scenes

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T15:26:06.226530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-09T20:06:15.236940Z digest=sha256:071a08376c21d6644c126f5404e7e965a47b124d60782f247125d8714a8a71ff

Observation b558a603-8823-4381-b28b-42bb8f28389c · inbound

SAMOFT: Robust Multi-Object Tracking via Region and Flow cites this paper.

SAMOFT: Robust Multi-Object Tracking via Region and Flow MOT20: A benchmark for multi object tracking in crowded scenes

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-12T03:11:19.003269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-05-12T03:09:39.668829Z digest=sha256:ea1cecdd7fa2e37f2a4761d46baefbb1ac12508951cbd89143c9cfde4a8fcc3e

Observation 70cdd1a4-4ab0-47a3-9093-0b22b7d1299a · inbound

Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset cites this paper.

Face versus Body Tracking for Human-Robot Interaction: An Egocentric Dataset MOT20: A benchmark for multi object tracking in crowded scenes

Reference 28

Resolution
verified exact
arxiv_id, observed 2026-07-02T03:36:30.378912Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-28T09:47:58.384726Z digest=sha256:2ed6c19f70045ffe4ac59ba7851e8a3aa55f5731eb152b1b7ef54f7a7222a8a8

Observation 3cfd8709-4ccd-4893-806d-d31012645932 · inbound

Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking cites this paper.

Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-07-04T10:39:45.812702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-26T08:35:55.702038Z digest=sha256:b12f7a5cc9b5f0a850591d97c4ddb5db1f412efa12c4afa363abcee2a7b3dcf5

Observation 185a72f0-51ac-4f05-9bda-5b7c0f1b6253 · inbound

Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking cites this paper.

Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 25

Resolution
unresolved
no resolver link, observed 2026-07-12T12:41:05.803889Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T12:41:05.803889Z digest=sha256:c64b5aaf753de96697bee70ee6d62831cedc258ca185f9185af9e9feb2297cc7

Observation 25aab360-0e82-4144-bc65-f499ac0bd305 · inbound

Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking cites this paper.

Polycepta: Object-Centric Appearance Estimation for Multi-Object Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 25

Resolution
unresolved
no resolver link, observed 2026-07-15T10:36:21.930108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-15T10:36:21.930108Z digest=sha256:af15e08de43c97de008caa99c6435310493959e4ca2aa2a8f4ef5e01b928baff

Observation ec17facb-0ecf-4fd3-8aac-e6de29422192 · inbound

PS-MOT: Cultivating Instance Awareness from Point Seeds for Multi-Object Tracking cites this paper.

PS-MOT: Cultivating Instance Awareness from Point Seeds for Multi-Object Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 15

Resolution
metadata mismatch
arxiv_id, observed 2026-06-30T06:54:20.981982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-06-30T06:46:14.722762Z digest=sha256:b95f718f05d836fe4b6975c11838cedce48886630e9f3ca16f1ffe5dcc99ada8

Observation d6ef136a-56d9-47f5-9a3b-5b0deb0a9d18 · inbound

WaspMOT: A Benchmark for Long-Term Multi-Object Tracking of Trichogramma Wasps cites this paper.

WaspMOT: A Benchmark for Long-Term Multi-Object Tracking of Trichogramma Wasps MOT20: A benchmark for multi object tracking in crowded scenes

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-07-10T02:26:43.123831Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

source=pdf_text observed=2026-07-10T02:17:23.420127Z digest=sha256:1506025c208444f0da3c3bde3612d6f118751a0997bbb62aeca95da9a964006f

Observation 7ff4a9cd-3a12-4c91-ac00-14c172225c8e · inbound

WaspMOT: A Benchmark for Long-Term Multi-Object Tracking of Trichogramma Wasps cites this paper.

WaspMOT: A Benchmark for Long-Term Multi-Object Tracking of Trichogramma Wasps MOT20: A benchmark for multi object tracking in crowded scenes

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-02T07:50:27.541867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T07:50:27.541867Z digest=sha256:472e519a69e0537a07283224e22550f81019adf836ecb56621efa97333e46883

Observation 10d03cc7-291a-4d73-8f77-daebfc77aaf2 · inbound

Higher-Order Cell Tracking Transformer cites this paper.

Higher-Order Cell Tracking Transformer MOT20: A benchmark for multi object tracking in crowded scenes

Reference 13

Resolution
unresolved
no resolver link, observed 2026-07-14T03:25:28.859078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-14T03:25:28.859078Z digest=sha256:27c88f00df7931a7fa63f73237ddadc6deaff8c4f5a973c864a610ccabebfb52

Observation cba77c81-db88-48ac-a084-fed526459fd0 · inbound

Incremental Optimal Assignment for Real-Time Crowd Tracking cites this paper.

Incremental Optimal Assignment for Real-Time Crowd Tracking MOT20: A benchmark for multi object tracking in crowded scenes

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-01T07:45:11.508329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T07:45:11.508329Z digest=sha256:64a9e33efe75600185161ab64ff5b62f6da527af9af912ff3054a0e9d9696662

Observation 74ff8c51-5b3e-4c78-bedb-edfde0a80c65 · inbound

GenTrack3: Hybrid Stochastic-Deterministic Online Multi-Object Tracking with Cluster-Aware Association cites this paper.

GenTrack3: Hybrid Stochastic-Deterministic Online Multi-Object Tracking with Cluster-Aware Association MOT20: A benchmark for multi object tracking in crowded scenes

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-11T14:42:30.226525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T14:42:30.226525Z digest=sha256:ac979f5955e5c9a902b297ad4abfa838295bb6e2220230f97c7bf0ccab153b14