MFT: Long-Term Tracking of Every Pixel
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00373794" target="_blank" >RIV/68407700:21230/24:00373794 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/WACV57701.2024.00669" target="_blank" >https://doi.org/10.1109/WACV57701.2024.00669</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/WACV57701.2024.00669" target="_blank" >10.1109/WACV57701.2024.00669</a>
Alternative languages
Result language
angličtina
Original language name
MFT: Long-Term Tracking of Every Pixel
Original language description
We propose MFT -- Multi-Flow dense Tracker -- a novel method for dense, pixel-level, long-term tracking. The approach exploits optical flows estimated not only between consecutive frames, but also for pairs of frames at logarithmically spaced intervals. It selects the most reliable sequence of flows on the basis of estimates of its geometric accuracy and the probability of occlusion, both provided by a pre-trained CNN. We show that MFT achieves competitive performance on the TAP-Vid benchmark, outperforming baselines by a significant margin, and tracking densely orders of magnitude faster than the state-of-the-art point-tracking methods. The method is insensitive to medium-length occlusions and it is robustified by estimating flow with respect to the reference frame, which reduces drift.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
ISBN
979-8-3503-1892-0
ISSN
2472-6737
e-ISSN
2642-9381
Number of pages
11
Pages from-to
6823-6833
Publisher name
IEEE
Place of publication
Piscataway
Event location
Waikoloa, HI, USA
Event date
Jan 4, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001222964606093