Methods › Computer Vision › Multi-Object Tracking Models › FairMOT

FairMOT

7 papers tagged archive 2025-07-28

Introduced by Yifu Zhang et al. in FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

FairMOT is a model for multi-object tracking which consists of two homogeneous branches to predict pixel-wise objectness scores and re-ID features. The achieved fairness between the tasks is used to achieve high levels of detection and tracking accuracy. The detection branch is implemented in an anchor-free style which estimates object centers and sizes represented as position-aware measurement maps. Similarly, the re-ID branch estimates a re-ID feature for each pixel to characterize the object centered at the pixel. Note that the two branches are completely homogeneous which essentially differs from the previous methods which perform detection and re-ID in a cascaded style. It is also worth noting that FairMOT operates on high-resolution feature maps of strides four while the previous anchor-based methods operate on feature maps of stride 32. The elimination of anchors as well as the use of high-resolution feature maps better aligns re-ID features to object centers which significantly improves the tracking accuracy.

PaperSource

Papers archive 2025-07-28

7 shown of 7, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

16 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Object Tracking6
Multi-Object Tracking5
Multiple Object Tracking4
Object Detection3
Object2
Adversarial Attack1
Attribute1
Disaster Response1
Fairness1
Knowledge Distillation1
Multi-Task Learning1
Prediction1
Shadow Detection1
Trajectory Forecasting1
motion prediction1
object-detection1

Usage over time archive 2025-07-28

Papers per year tagged with FairMOT: 2020 to 2024, peak 2 2 0 2020: 1 paper 2020 2021: 2 papers 2021 2022: 2 papers 2022 2023: 1 paper 2023 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (7 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Multi-Object Tracking Models

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