Bounding Box Detection in Visual Tracking: Measurement Model Parameter Estimation

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969676" target="_blank" >RIV/49777513:23520/23:43969676 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION52260.2023.10224194" target="_blank" >https://doi.org/10.23919/FUSION52260.2023.10224194</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION52260.2023.10224194" target="_blank" >10.23919/FUSION52260.2023.10224194</a>

Result language
angličtina
Original language name
Bounding Box Detection in Visual Tracking: Measurement Model Parameter Estimation
Original language description
Common visual tracking algorithms make use of measurement models whose parameters need to be specified. These are, namely, measurement noise covariance related to spatial error of detections provided by a visual detection algorithm, probability of detection, and expected number of clutter detections. The measurement model parameters are often hand selected, using no data-based knowledge. This paper proposes a technique to estimate the parameters by reliably associating detections to annotations in each video frame. The technique is verified on the publicly available MOT-17 dataset.
Czech name
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Czech description
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Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 2023 26th International Conference on Information Fusion, FUSION 2023
ISBN
979-8-89034-485-4
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
Charleston, Jižní Karolína, USA
Event location
Charleston, Jižní Karolína, USA
Event date
Jun 27, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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