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Visual Tracking

202 papers with code · 10 benchmarks · 28 datasets archive 2025-07-28

Computer Vision

Visual Tracking is an essential and actively researched problem in the field of computer vision with various real-world applications such as robotic services, smart surveillance systems, autonomous driving, and human-computer interaction. It refers to the automatic estimation of the trajectory of an arbitrary target object, usually specified by a bounding box in the first frame, as it moves around in subsequent video frames.

Source: Learning Reinforced Attentional Representation for End-to-End Visual Tracking

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

10 leaderboard tables shown for this task, 10 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

DatasetBest model (first row in archive order)PaperCodeSyntologyCompare
TNL2K (6 rows) ARTrack-L Autoregressive Visual Tracking code — Compare
DAVIS (2 rows) TAPIR (Panning MOVi-E) TAPIR: Tracking Any Point with per-frame Initialization and... code Syntology ran 0 of 1 samples · 1 unverified Compare
Kinetics (2 rows) TAPIR (Panning MOVi-E) TAPIR: Tracking Any Point with per-frame Initialization and... code Syntology ran 0 of 1 samples · 1 unverified Compare
Kubric (2 rows) TAPIR (Panning MOVi-E) TAPIR: Tracking Any Point with per-frame Initialization and... code Syntology ran 0 of 1 samples · 1 unverified Compare
RGB-Stacking (2 rows) TAPIR (MOVi-E) TAPIR: Tracking Any Point with per-frame Initialization and... code Syntology ran 0 of 1 samples · 1 unverified Compare
LaSOT (1 row) TATrack-L Target-Aware Tracking with Long-term Context Attention code Syntology ran 0 of 2 samples · 2 unverified Compare
OTB-100 (1 row) SiamFC-lu (Ours) Learning to Update for Object Tracking with Recurrent Meta-learner — — Compare
OTB-2013 (1 row) SiamFC-lu (Ours) Learning to Update for Object Tracking with Recurrent Meta-learner — — Compare
Second dialogue state tracking challenge (1 row) MDNet Learning Multi-Domain Convolutional Neural Networks for Visual Tracking code — Compare
TrackingNet (1 row) TATrack-L Target-Aware Tracking with Long-term Context Attention code Syntology ran 0 of 2 samples · 2 unverified Compare

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

28 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

4 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 202 papers with code (525 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

Syntology lines on 10 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

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