Model-Based Multi-Object Visual Tracking: Identification and Standard Model Limitations
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976506" target="_blank" >RIV/49777513:23520/25:43976506 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION65864.2025.11124146" target="_blank" >https://doi.org/10.23919/FUSION65864.2025.11124146</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION65864.2025.11124146" target="_blank" >10.23919/FUSION65864.2025.11124146</a>
Alternative languages
Result language
angličtina
Original language name
Model-Based Multi-Object Visual Tracking: Identification and Standard Model Limitations
Original language description
This paper uses multi-object tracking methods known from the radar tracking community to address the problem of pedestrian tracking using 2D bounding box detections. The standard point-object (SPO) model is adopted, and the posterior density is computed using the Poisson multi-Bernoulli mixture (PMBM) filter. The selection of the model parameters rooted in continuous time is discussed, including the birth and survival probabilities. Some parameters are selected from the first principles, while others are identified from the data, which is, in this case, the publicly available MOT-17 dataset. Although the resulting PMBM algorithm yields promising results, a mismatch between the SPO model and the data is revealed. The model-based approach assumes that modifying the problematic components causing the SPO model-data mismatch will lead to better modelbased algorithms in future developments.
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
20205 - Automation and control systems
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 28th International Conference on Information Fusion (FUSION)
ISBN
978-1-03-705623-9
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
Rio de Janiero, Brazílie
Event location
Rio de Janiero, Brazílie
Event date
Jul 7, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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