How to make an RGBD tracker?
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F19%3A00337832" target="_blank" >RIV/68407700:21230/19:00337832 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-11009-3_8" target="_blank" >https://link.springer.com/chapter/10.1007%2F978-3-030-11009-3_8</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-030-11009-3_8" target="_blank" >10.1007/978-3-030-11009-3_8</a>
Alternative languages
Result language
angličtina
Original language name
How to make an RGBD tracker?
Original language description
We propose a generic framework for converting an arbitrary short-term RGB tracker into an RGBD tracker. The proposed framework has two mild requirements – the short-term tracker provides a bounding box and its object model update can be stopped and resumed. The core of the framework is a depth augmented foreground segmentation which is formulated as an energy minimization problem solved by graph cuts. The proposed framework offers two levels of integration. The first requires that the RGB tracker can be stopped and resumed according to the decision on target visibility. The level-two integration requires that the tracker accept an external mask (foreground region) in the target update. We integrate in the proposed framework the Discriminative Correlation Filter (DCF), and three state-of-the-art trackers – Efficient Convolution Operators for Tracking (ECOhc, ECOgpu) and Discriminative Correlation Filter with Channel and Spatial Reliability (CSR-DCF). Comprehensive experiments on Princeton Tracking Benchmark (PTB) show that level-one integration provides significant improvements for all trackers: DCF average rank improves from 18th to 17th, ECOgpu from 16th to 10th, ECOhc from 15th to 5th and CSR-DCF from 19th to 14th. CSR-DCF with level-two integration achieves the top rank by a clear margin on PTB. Our framework is particularly powerful in occlusion scenarios where it provides 13.5% average improvement and 26% for the best tracker (CSR-DCF).
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
<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)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computer Vision – ECCV 2018 Workshops
ISBN
978-3-030-11008-6
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
14
Pages from-to
148-161
Publisher name
Springer
Place of publication
Basel
Event location
Munich
Event date
Sep 8, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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