Feature-Based Multi-Object Tracking With Maximally One Object per Class

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F22%3A43965987" target="_blank" >RIV/49777513:23520/22:43965987 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11140/22:10448579
Result on the web
<a href="https://dx.doi.org/10.23919/FUSION49751.2022.9841332" target="_blank" >https://dx.doi.org/10.23919/FUSION49751.2022.9841332</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION49751.2022.9841332" target="_blank" >10.23919/FUSION49751.2022.9841332</a>

Result language
angličtina
Original language name
Feature-Based Multi-Object Tracking With Maximally One Object per Class
Original language description
This paper deals with the problem of tracking multiple objects, in which each object is known to belong to a unique class. We follow the tracking by detection paradigm and assume that the object detector provides scores in addition to each detection. The problem is tackled as simultaneous classification and tracking using random finite sets. Inspired by the multi-Bernoulli mixture (MBM) filter, we propose a solution to the problem by modifying the target birth process. To simplify the implementation and to mitigate the computational costs, we develop tractable solutions with linear complexity. The algorithms are subsequently used for visual tracking of surgical instruments. As a by-product, we derive the prediction step of the Bernoulli filter using the probability generating functionals (PGFLs).
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 25th International Conference on Information Fusion, FUSION 2022
ISBN
978-1-73774-972-1
ISSN
—
e-ISSN
—
Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
New York
Event location
Linköping, Sweden
Event date
Jul 4, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000855689000104

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