Hierarchical Motion Tracking Using Matching of Sparse Features
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F18%3A00330726" target="_blank" >RIV/68407700:21240/18:00330726 - isvavai.cz</a>
Alternative codes found
RIV/67985807:_____/18:00497814
Result on the web
<a href="http://dx.doi.org/10.1109/SITIS.2018.00075" target="_blank" >http://dx.doi.org/10.1109/SITIS.2018.00075</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/SITIS.2018.00075" target="_blank" >10.1109/SITIS.2018.00075</a>
Alternative languages
Result language
angličtina
Original language name
Hierarchical Motion Tracking Using Matching of Sparse Features
Original language description
Fundamental approaches in motion tracking are based on registration of pixel patches from one frame to another. To ensure invariance to some changes in the image and improve the speed of discovering a match, a pyramidal approach is used to steer the process faster to optima. However, registration of the patches in high resolution is still computationally expensive. Because we require the algorithm to process Ultra HD video content in real time on commonly available hardware, especially on mid-tier graphics processing units, approaches using matching of pixel patches are not feasible. In this paper, we present and evaluate an approach inspired by motion tracking on an image pyramid. However, instead of comparing pixel patches one to another, we utilise binary image descriptors that are much shorter and inherently use a Hamming distance for their direct comparison. Evaluation of our implementation, which is available on GitHub, was carried out on the Multiple Object Tracking challenge dataset.
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/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)
ISBN
978-1-5386-9385-8
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
449-456
Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Las Palmas de Gran Canaria
Event date
Nov 26, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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