MFTIQ: Multi-Flow Tracker with Independent Matching Quality Estimation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00384987" target="_blank" >RIV/68407700:21230/25:00384987 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/WACV61041.2025.00784" target="_blank" >https://doi.org/10.1109/WACV61041.2025.00784</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/WACV61041.2025.00784" target="_blank" >10.1109/WACV61041.2025.00784</a>
Alternative languages
Result language
angličtina
Original language name
MFTIQ: Multi-Flow Tracker with Independent Matching Quality Estimation
Original language description
In this work, we present MFTIQ, a novel dense long-term tracking model that advances the Multi-Flow Tracker (MFT) framework to address challenges in point-level visual tracking in video sequences. MFTIQ builds upon the flow-chaining concepts of MFT, integrating an Independent Quality (IQ) module that separates correspondence quality estimation from optical flow computations. This decoupling significantly enhances the accuracy and flexibility of the tracking process, allowing MFTIQ to maintain reliable trajectory predictions even in scenarios of prolonged occlusions and complex dynamics. Designed to be "plug-and-play", MFTIQ can be employed with any off-the-shelf optical flow method without the need for fine-tuning or architectural modifications. Experimental validations on the TAPVid Davis dataset show that MFTIQ with RoMa [16] optical flow not only surpasses MFT but also performs comparably to state-of-the-art trackers while having substantially faster processing speed. Code and models available at https://github.com/serycjon/MFTIQ
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
ISBN
979-8-3315-1084-8
ISSN
2472-6737
e-ISSN
2642-9381
Number of pages
11
Pages from-to
8079-8089
Publisher name
IEEE
Place of publication
Piscataway
Event location
Tucson
Event date
Feb 28, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001521272600294