Model-Based Multi-Object Visual Tracking: Identification and Standard Model Limitations

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Model-Based Multi-Object Visual Tracking: Identification and Standard Model Limitations

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Model-Based Multi-Object Visual Tracking: Identification and Standard Model Limitations

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2025 28th International Conference on Information Fusion (FUSION)

ISBN

978-1-03-705623-9

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

1-8

Název nakladatele

IEEE

Místo vydání

Rio de Janiero, Brazílie

Místo konání akce

Rio de Janiero, Brazílie

Datum konání akce

7. 7. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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