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Paper Citation Record · LEDGER

High Performance Visual Object Tracking with Unified Convolutional Networks

As of 22 August 2026, this Paper Citation Record lists 74 of 74 outbound references and 0 inbound Pith citation observations for arXiv:1908.09445.

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

pith.paper-citation-record.v1
1908.09445 v1

Coverage vector

measured 74 of 74 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:17:08.029962Z

measured 74 of 74 standing notices

One-hop event checks from named stored sources.

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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

74 of 74 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 87dc72cb-08a0-4c84-82e4-859635e6bcce · outbound

This paper cites Sparse representation for crowd attributes recognition,.

High Performance Visual Object Tracking with Unified Convolutional Networks Sparse representation for crowd attributes recognition,

Reference 1

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Observation 20d2518f-e000-4161-b815-7c491f421d85 · outbound

This paper cites Research on automatic parking systems based on parking scene recognition,.

High Performance Visual Object Tracking with Unified Convolutional Networks Research on automatic parking systems based on parking scene recognition,

Reference 2

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Observation c6183c24-6565-4203-b43c-9b3bd6af0c17 · outbound

This paper cites FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks.

High Performance Visual Object Tracking with Unified Convolutional Networks FastPose: Towards Real-time Pose Estimation and Tracking via Scale-normalized Multi-task Networks

Reference 3

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Observation 47025c46-861c-4583-a4df-5e9198e3a48a · outbound

This paper cites Exploiting Offset-guided Network for Pose Estimation and Tracking.

High Performance Visual Object Tracking with Unified Convolutional Networks Exploiting Offset-guided Network for Pose Estimation and Tracking

Reference 4

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Observation 91b9b435-4e14-4bdf-922a-5d60658753c5 · outbound

This paper cites State-aware re-identification feature for multi-target multi-camera tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks State-aware re-identification feature for multi-target multi-camera tracking,

Reference 5

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Observation 7919de92-26c8-47bc-be3f-91d198c53631 · outbound

This paper cites Selection of observation position and orientation in visual servoing with eye-in-vehicle configuration for manipulator,.

High Performance Visual Object Tracking with Unified Convolutional Networks Selection of observation position and orientation in visual servoing with eye-in-vehicle configuration for manipulator,

Reference 6

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Observation a7acd3d5-c318-4316-9ed9-26925e1372f5 · outbound

This paper cites Adaptive tra- jectory tracking of wheeled mobile robots based on a fish-eye camera,.

High Performance Visual Object Tracking with Unified Convolutional Networks Adaptive tra- jectory tracking of wheeled mobile robots based on a fish-eye camera,

Reference 7

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Observation e3953693-da33-493d-a578-4fdf7e34b253 · outbound

This paper cites A velocity compensation visual servo method for oculomotor con- trol of bionic eyes,.

High Performance Visual Object Tracking with Unified Convolutional Networks A velocity compensation visual servo method for oculomotor con- trol of bionic eyes,

Reference 8

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Observation c9c406f5-8b45-429d-9476-70475acb0db1 · outbound

This paper cites Motion control in saccade and smooth pursuit for bionic eye based on three- dimensional coordinates,.

High Performance Visual Object Tracking with Unified Convolutional Networks Motion control in saccade and smooth pursuit for bionic eye based on three- dimensional coordinates,

Reference 9

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Observation 0e75d2bd-e359-4887-895d-df3aaadd3840 · outbound

This paper cites Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes.

High Performance Visual Object Tracking with Unified Convolutional Networks Optical Flow Based Real-time Moving Object Detection in Unconstrained Scenes

Reference 10

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Observation 4f9a07b4-85de-4da3-90d4-d4031cdd22d8 · outbound

This paper cites Optical Flow Based Online Moving Foreground Analysis.

High Performance Visual Object Tracking with Unified Convolutional Networks Optical Flow Based Online Moving Foreground Analysis

Reference 11

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Observation 7d5e4bde-e85f-4f50-9a79-a797c37f8497 · outbound

This paper cites An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes.

High Performance Visual Object Tracking with Unified Convolutional Networks An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes

Reference 12

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Observation 06d3cafe-3ec7-487a-b43e-bab80db6bde6 · outbound

This paper cites Motion cue based instance-level moving object detection,.

High Performance Visual Object Tracking with Unified Convolutional Networks Motion cue based instance-level moving object detection,

Reference 13

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Observation 158cf080-787a-4385-a2fe-fb02f628ca85 · outbound

This paper cites Std: A stereo tracking dataset for evaluating binocular tracking algorithms,.

High Performance Visual Object Tracking with Unified Convolutional Networks Std: A stereo tracking dataset for evaluating binocular tracking algorithms,

Reference 14

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Observation 4b2bd6df-fc70-47a9-a0fe-7b17d6f2dc0c · outbound

This paper cites Object tracking benchmark,.

High Performance Visual Object Tracking with Unified Convolutional Networks Object tracking benchmark,

Reference 15

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Observation 0c018eed-2d76-420e-9e52-18203ec62069 · outbound

This paper cites Visual tracking: An exper- imental survey,.

High Performance Visual Object Tracking with Unified Convolutional Networks Visual tracking: An exper- imental survey,

Reference 16

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Observation 525c7741-5105-45d0-9145-8361cb4cf6a6 · outbound

This paper cites The sixth visual object tracking vot2018 challenge results,.

High Performance Visual Object Tracking with Unified Convolutional Networks The sixth visual object tracking vot2018 challenge results,

Reference 17

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Observation d1b55746-5770-45a5-9cd9-211d560535ad · outbound

This paper cites The visual object tracking vot2015 challenge results,.

High Performance Visual Object Tracking with Unified Convolutional Networks The visual object tracking vot2015 challenge results,

Reference 18

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Observation 1fc7a8ce-02b7-4636-9c3e-be2c63c3355b · outbound

This paper cites Online object tracking with sparse prototypes,.

High Performance Visual Object Tracking with Unified Convolutional Networks Online object tracking with sparse prototypes,

Reference 19

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Observation 117aefe4-78d9-4ff4-87fe-ff4d20779410 · outbound

This paper cites Inverse sparse tracker with a locally weighted distance metric,.

High Performance Visual Object Tracking with Unified Convolutional Networks Inverse sparse tracker with a locally weighted distance metric,

Reference 20

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Observation 3594f549-7a50-4f6d-a811-025ef1389b56 · outbound

This paper cites Struck: Structured output tracking with kernels,.

High Performance Visual Object Tracking with Unified Convolutional Networks Struck: Structured output tracking with kernels,

Reference 21

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Observation 5bce6619-c19b-443c-ba46-0dc14f649b12 · outbound

This paper cites Real-time tracking via on-line boosting,.

High Performance Visual Object Tracking with Unified Convolutional Networks Real-time tracking via on-line boosting,

Reference 22

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Observation e321f8dc-f5c6-47d0-bc19-af9d7508dd28 · outbound

This paper cites Robust object tracking with online multiple instance learning,.

High Performance Visual Object Tracking with Unified Convolutional Networks Robust object tracking with online multiple instance learning,

Reference 23

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Observation 69a653e1-065f-48f2-98fd-ba25adbbefc4 · outbound

This paper cites High-speed tracking with kernelized correlation filters,.

High Performance Visual Object Tracking with Unified Convolutional Networks High-speed tracking with kernelized correlation filters,

Reference 24

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Observation d3853206-34b1-45ad-8338-b863104e1dd6 · outbound

This paper cites Learning spatially regularized correlation filters for vi- sual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning spatially regularized correlation filters for vi- sual tracking,

Reference 25

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Observation 2582b3f7-1300-404a-9d6d-f15cb7f08f0a · outbound

This paper cites Long- term correlation tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Long- term correlation tracking,

Reference 26

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Observation b91dde52-a88d-4fbf-a63c-0dcfc19ccc18 · outbound

This paper cites A scale adaptive kernel correlation filter tracker with feature integration,.

High Performance Visual Object Tracking with Unified Convolutional Networks A scale adaptive kernel correlation filter tracker with feature integration,

Reference 27

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Observation dff9d339-d178-4edc-a1f6-082205d28fb3 · outbound

This paper cites Ex- ploiting the circulant structure of tracking-by-detection with kernels,.

High Performance Visual Object Tracking with Unified Convolutional Networks Ex- ploiting the circulant structure of tracking-by-detection with kernels,

Reference 28

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Observation 598da227-3b51-4f44-a509-e50354d77173 · outbound

This paper cites Discriminative scale space tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Discriminative scale space tracking,

Reference 29

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Observation edb419b0-39b5-4b62-9739-7aa0b84ec67f · outbound

This paper cites Learning background-aware correlation filters for visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning background-aware correlation filters for visual tracking,

Reference 30

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Observation 079640e9-feef-4286-a946-46589021d212 · outbound

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High Performance Visual Object Tracking with Unified Convolutional Networks Imagenet classification with deep convolutional neural networks,

Reference 31

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This paper cites Deep resid- ual learning for image recognition,.

High Performance Visual Object Tracking with Unified Convolutional Networks Deep resid- ual learning for image recognition,

Reference 32

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Observation b165e19c-e904-4311-ba21-23e3bf5c4ec8 · outbound

This paper cites Faster r-cnn: towards real-time object detection with region proposal networks,.

High Performance Visual Object Tracking with Unified Convolutional Networks Faster r-cnn: towards real-time object detection with region proposal networks,

Reference 33

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Observation 759d49eb-a29b-4c78-b17b-0bceeb8e8351 · outbound

This paper cites Fully convolu- tional networks for semantic segmentation,.

High Performance Visual Object Tracking with Unified Convolutional Networks Fully convolu- tional networks for semantic segmentation,

Reference 34

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Observation 8e826822-3165-4480-ae53-393843f68501 · outbound

This paper cites Hi- erarchical convolutional features for visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Hi- erarchical convolutional features for visual tracking,

Reference 35

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raw_fallback, observed 2026-08-14T11:17:08.892910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.813944Z digest=sha256:d3dd333b3aa12e7808de15eb0d4cbf1de27ddd67a00fae31726ff12586bc99b9

Observation 58e88c15-749f-4f3e-968a-4ca7b4053dc0 · outbound

This paper cites Hedged deep tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Hedged deep tracking,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.875289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.819171Z digest=sha256:7bcc33c65582327dc8740e67c6d2e0f0bee9231321568e699168007b9d35b51c

Observation bfa5d6aa-6328-4bb8-b3b3-9099b9b3ef4d · outbound

This paper cites Beyond correlation filters: Learning continuous convo- lution operators for visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Beyond correlation filters: Learning continuous convo- lution operators for visual tracking,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.859523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.824230Z digest=sha256:b0f869d05edf7fca6714f117d7cdeaaae03618fe9f9401f00f2e4d96facb7475

Observation 739597ea-b92b-4d32-97dd-0a1336e94372 · outbound

This paper cites Convolutional features for correlation filter based visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Convolutional features for correlation filter based visual tracking,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.842836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.828960Z digest=sha256:74e588ffd67928a65a9d0ba1a75847f0e984542f7802f77f6eb03fde551022da

Observation 9a16c30e-9720-4ee1-ae50-f14fb7e618d1 · outbound

This paper cites Fully-convolutional siamese networks for object tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Fully-convolutional siamese networks for object tracking,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.827365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.834746Z digest=sha256:36bd921d6ba264d57c959f8bed3c1dfd2bd5be7a2d89902cfe700d77fdcb05c1

Observation 8a6a37b0-9c88-4045-ad0a-918c28865c0d · outbound

This paper cites Visual track- ing with fully convolutional networks,.

High Performance Visual Object Tracking with Unified Convolutional Networks Visual track- ing with fully convolutional networks,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.811653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.840000Z digest=sha256:826a2377a01c8d6d54f97d68ed3f93226262e02a9ca1f6b1d083a3e3ce7b59a3

Observation 48a08e09-35f5-470a-bb66-bb26c2d491fa · outbound

This paper cites The visual object tracking vot2016 challenge results,.

High Performance Visual Object Tracking with Unified Convolutional Networks The visual object tracking vot2016 challenge results,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.795131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.845477Z digest=sha256:b8725c0c41c0c2f95f57c7f63001cf0c27f29a5c501a74968794863a00e7eb4b

Observation c9feeb1c-b3b6-41ab-977a-4382d2518d7e · outbound

This paper cites UCT: Learning unified convolutional networks for real-time visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks UCT: Learning unified convolutional networks for real-time visual tracking,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.778025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.850551Z digest=sha256:868993bdb1522d0946b04178d81327e3fb838287ecef903a8a57365eb8c5c1c5

Observation 84de5e56-0774-4b42-be42-c107f6219ab6 · outbound

This paper cites Two-stream gated fusion convnets for action recognition,.

High Performance Visual Object Tracking with Unified Convolutional Networks Two-stream gated fusion convnets for action recognition,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.760643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.856703Z digest=sha256:9917058ac312210265ae8c866c08044d75c4a8a2f5b763e976b49a5a5d61548c

Observation fb4fcd4c-ddf7-4941-bf0e-e93adddd0c38 · outbound

This paper cites Attention-guided unified network for panoptic segmentation,.

High Performance Visual Object Tracking with Unified Convolutional Networks Attention-guided unified network for panoptic segmentation,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.744164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.861822Z digest=sha256:5e57d43d541f239aa9510f2c8f3ae5d58784207ae79f83777a5950bd1bfa1a3d

Observation c2011070-9f42-496f-8d44-bbd205e4e1ee · outbound

This paper cites Action Machine: Rethinking Action Recognition in Trimmed Videos.

High Performance Visual Object Tracking with Unified Convolutional Networks Action Machine: Rethinking Action Recognition in Trimmed Videos

Reference 45

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:17:08.218197Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.867387Z digest=sha256:e67e0ef3e02b6c28ec942ef3760f397040f613a9035bd5cfb7b6170fc34fe4ba

Observation 0bc6f8fe-bc0c-43d0-8cda-bb0a37023410 · outbound

This paper cites Learning Gating ConvNet for Two-Stream based Methods in Action Recognition.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning Gating ConvNet for Two-Stream based Methods in Action Recognition

Reference 46

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:17:08.192418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.874594Z digest=sha256:011b3f75acb47e98d9bedbdaf1f28ebe67ca4cf9d56a6f8fad0a708f4a3d1d43

Observation 6247962e-d6a7-473f-b5af-c967d575ebd2 · outbound

This paper cites Transferring Rich Feature Hierarchies for Robust Visual Tracking.

High Performance Visual Object Tracking with Unified Convolutional Networks Transferring Rich Feature Hierarchies for Robust Visual Tracking

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.879815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.879815Z digest=sha256:7445c6c01b40248e2d08c9086ee4ec5acfba75593bb430d3a7f9521c07256aa2

Observation e08d70c3-93e7-4090-8288-c72e9b5de361 · outbound

This paper cites Deeptrack: Learning dis- criminative feature representations online for robust vi- sual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Deeptrack: Learning dis- criminative feature representations online for robust vi- sual tracking,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.728020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.886392Z digest=sha256:542545012db6cc7bb6a5adcde4832c4ab97f3389e00f023b59ca6fe69ae48a33

Observation cdb80473-b21a-45d7-945c-49db82877881 · outbound

This paper cites Multi-hierarchical Independent Correlation Filters for Visual Tracking.

High Performance Visual Object Tracking with Unified Convolutional Networks Multi-hierarchical Independent Correlation Filters for Visual Tracking

Reference 49

Resolution
verified exact
local_arxiv, observed 2026-08-14T11:17:08.142609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.891937Z digest=sha256:23e8209c82ba071a3f40043de0afc29f9077239e815a83bce025969470e5c597

Observation 381eeb05-b51f-490a-854f-8432390765df · outbound

This paper cites Learning multi-domain convolu- tional neural networks for visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning multi-domain convolu- tional neural networks for visual tracking,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.710872Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.897403Z digest=sha256:cdd60dc2b98d55044360fd6a86492924f89e79e8524d5d3594e416b388200831

Observation a78a8f58-6337-48f1-9447-26b2ce4ae1c9 · outbound

This paper cites Template matching using fast normalized cross correlation,.

High Performance Visual Object Tracking with Unified Convolutional Networks Template matching using fast normalized cross correlation,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.693155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.903539Z digest=sha256:75a3465f88a1b148ca3f9fbd444ab7a3d14ff98962dbebab0ed1a99ed6a041d7

Observation fb95a89c-28ce-4ccd-bf4f-37f12b0e89a8 · outbound

This paper cites Real-time track- ing of non-rigid objects using mean shift,.

High Performance Visual Object Tracking with Unified Convolutional Networks Real-time track- ing of non-rigid objects using mean shift,

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.676935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.908849Z digest=sha256:e2b58ea92b1fe850873e62df70ab9843dc9c087333e52bd055d2c47362e5e376

Observation d79c3d05-f987-40d3-b848-f8fa7237fa4b · outbound

This paper cites Visual object tracking using adaptive correlation filters,.

High Performance Visual Object Tracking with Unified Convolutional Networks Visual object tracking using adaptive correlation filters,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.659147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.914911Z digest=sha256:f363ab925dee4153bb0ba75bcaf9fb7e25cc6a564516c9859e1086b200accecc

Observation 5013d76e-0b68-44b7-850f-dacf3e48e415 · outbound

This paper cites Staple: Complementary learners for real- time tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Staple: Complementary learners for real- time tracking,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.642551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.920052Z digest=sha256:b3ff2413071356bf1e8126a5148d9eda35272379a12c68f699b8815c242277ab

Observation c1e1e120-c642-47e8-866d-d64c4d0a622d · outbound

This paper cites Correla- tion filters with limited boundaries,.

High Performance Visual Object Tracking with Unified Convolutional Networks Correla- tion filters with limited boundaries,

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.623865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.924981Z digest=sha256:fac2f800b745481c5d16ae93ddc5b6b69eabfb9e718b7abe3255b648d3d570de

Observation 7bc83923-2d69-4c7c-9a43-6ba62fc9ca4d · outbound

This paper cites Learning to track at 100 fps with deep regression networks,.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning to track at 100 fps with deep regression networks,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.606372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.929933Z digest=sha256:1ccb1462135d2b9b73714647808c7cea8052b568ef733a1cbb1d5ae12d52dac8

Observation 7cd4197c-f051-46e9-b08e-98b58740eab3 · outbound

This paper cites End-to-end representation learning for correlation filter based tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks End-to-end representation learning for correlation filter based tracking,

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.589204Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.934819Z digest=sha256:aa4c371980c4dce54a08d8ed866fd781100050f93ed38c174c105900b2929280

Observation 13b0742b-eedc-451d-adeb-9f81c497f0cd · outbound

This paper cites DCFNet: Discriminant Correlation Filters Network for Visual Tracking.

High Performance Visual Object Tracking with Unified Convolutional Networks DCFNet: Discriminant Correlation Filters Network for Visual Tracking

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.939798Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.939798Z digest=sha256:11ac933f22dfe09bf74b8b38188449ff3b82f3bc9d5aaebcaf26fdb6bc30fd68

Observation 00f792ba-cb4d-4e04-999d-47d32be98a31 · outbound

This paper cites End-to-end flow correlation tracking with spatial-temporal attention,.

High Performance Visual Object Tracking with Unified Convolutional Networks End-to-end flow correlation tracking with spatial-temporal attention,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.571266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.946546Z digest=sha256:885175cfbfd0cad4c4bdf5f8f5ab4ff56ecca4b9e54dcd3701fded00e7945ee3

Observation fd9a0de3-493c-47d4-b44b-086a0ab26ee9 · outbound

This paper cites End-to-end video-level representation learning for action recognition,.

High Performance Visual Object Tracking with Unified Convolutional Networks End-to-end video-level representation learning for action recognition,

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.552105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.951952Z digest=sha256:745521e95c2934f47849d13777f9d6b22fb86d1a413ca4a5ca9e410b2048a9b6

Observation 92f2473c-c0a6-4103-a3ac-18a2d0390494 · outbound

This paper cites High performance visual tracking with siamese region proposal network,.

High Performance Visual Object Tracking with Unified Convolutional Networks High performance visual tracking with siamese region proposal network,

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.957083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.957083Z digest=sha256:c6fc11d167b764c830d7f7b8a1434328e689de377ba3b66de75dae944248dbf8

Observation c3b2abea-a567-49ae-ac17-aeae9e194aec · outbound

This paper cites Distractor-aware siamese networks for visual object tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Distractor-aware siamese networks for visual object tracking,

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.523405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.961774Z digest=sha256:8de09f64bdce40065b266c852af0038bf25d3c01a203c867d5817d9a9c6cf91b

Observation 887c4d53-a424-49f9-8737-9bbf5e40114a · outbound

This paper cites DenseBox: Unifying Landmark Localization with End to End Object Detection.

High Performance Visual Object Tracking with Unified Convolutional Networks DenseBox: Unifying Landmark Localization with End to End Object Detection

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.966774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.966774Z digest=sha256:aa4bf9d2ced31aca472500b55f9b1daec34f8b9d503859878a274f59cee61045

Observation 7b19bb41-f9b4-4255-9c10-3c32629288ed · outbound

This paper cites Learning a deep compact im- age representation for visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Learning a deep compact im- age representation for visual tracking,

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.506636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.973849Z digest=sha256:01241b09c6f9dd8ddac00274c070e4d667c1104952dda8983fc8d48776f7505a

Observation e4db4af5-dd26-4ca8-8efd-f608dd0eeb55 · outbound

This paper cites Online object tracking: A benchmark,.

High Performance Visual Object Tracking with Unified Convolutional Networks Online object tracking: A benchmark,

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.489457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.979411Z digest=sha256:3076e45bce0bcd53129c1293b9a9995834439e2a1e0e62485ad7e189e0d755bc

Observation 4edab244-8a2b-4685-8d26-0a5f1ee66768 · outbound

This paper cites The visual object tracking vot2015 challenge results,.

High Performance Visual Object Tracking with Unified Convolutional Networks The visual object tracking vot2015 challenge results,

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.471986Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.984416Z digest=sha256:ba4f1ad2d6f090224e7265eda13da329b7ff51ca40c65196278f4155b69c72df

Observation ca3dfd43-e18b-442e-9fcc-d20bf87010ed · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

High Performance Visual Object Tracking with Unified Convolutional Networks Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 67

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:07.989372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:07.989372Z digest=sha256:ee180e8fca729228685b493250e8b0afde887c7e69f3884d74461932647c689a

Observation 0db610fd-45c2-4fb2-b6e3-3be25ab0dae3 · outbound

This paper cites Visualizing and under- standing convolutional networks,.

High Performance Visual Object Tracking with Unified Convolutional Networks Visualizing and under- standing convolutional networks,

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.453996Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:07.995746Z digest=sha256:e6f7c2ffe0e9e3b8edec6272691ae0d4eebd6ddf5c131fbc79a86a6af4c44226

Observation c5b0470c-3b0a-4e0d-931c-183098b54b7a · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge,.

High Performance Visual Object Tracking with Unified Convolutional Networks ImageNet Large Scale Visual Recognition Challenge,

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-14T11:17:08.001388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T11:17:08.001388Z digest=sha256:98c6e1eeff155ed03b31e14a213820af34bfa265bc6d54064dab7769e6fd9941

Observation 182d9cd6-168f-4a6f-8297-8492ce0171d3 · outbound

This paper cites Caffe: Convolutional architecture for fast feature embedding,.

High Performance Visual Object Tracking with Unified Convolutional Networks Caffe: Convolutional architecture for fast feature embedding,

Reference 70

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.424373Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:08.006525Z digest=sha256:156da03fc28f6c747a9fcb131c982323e85964fafc0267793f5d7fc5d407280f

Observation 5d3783e9-1cc2-49e7-8e2c-7c10b24658b0 · outbound

This paper cites Parallel tracking and verifying: A framework for real-time and high accuracy visual tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Parallel tracking and verifying: A framework for real-time and high accuracy visual tracking,

Reference 71

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.407189Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:08.011662Z digest=sha256:4348a50677cb62b514598fb69046f63f78a88766d44795d61ba3e0dfbb52783d

Observation bc4fa692-eb39-4a0a-be0f-6160fba59790 · outbound

This paper cites Context-aware correlation filter tracking,.

High Performance Visual Object Tracking with Unified Convolutional Networks Context-aware correlation filter tracking,

Reference 72

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.390072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:08.018405Z digest=sha256:078f1a882d8ad9c8676ef78939756416a87fa33ad670149fd38be48034439b5e

Observation 412f9d43-3d23-4d9a-abf4-779c304d3932 · outbound

This paper cites Discriminative correlation filter with channel and spatial reliability,.

High Performance Visual Object Tracking with Unified Convolutional Networks Discriminative correlation filter with channel and spatial reliability,

Reference 73

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.373346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:08.024618Z digest=sha256:85a62bf3eb76c9f17c5a852c5023c1a562cf80e5af41918586d0b48c53d29ecb

Observation 8fa5bb61-502f-4c70-9749-888e50f4bb9d · outbound

This paper cites Visual tracking using attention- modulated disintegration and integration,.

High Performance Visual Object Tracking with Unified Convolutional Networks Visual tracking using attention- modulated disintegration and integration,

Reference 74

Resolution
verified fuzzy
raw_fallback, observed 2026-08-14T11:17:08.355127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-08-14T11:17:08.029962Z digest=sha256:23da1447041b61a8935a9db5649ba4a4bbd7c18278fd404b14bfeb9d721de934

Pith citing papers

No inbound Pith citation observations are available.