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Adaptive Method for Modeling of Temporal Dependencies between Fields of Vision in Multi-Camera Surveillance Systems

Abstrakt

A method of modeling the time of object transition between given pairs of cameras based on the Gaussian Mixture Model (GMM) is proposed in this article. Temporal dependencies modeling is a part of object re-identification based on the multi-camera experimental framework. The previously utilized Expectation-Maximization (EM) approach, requiring setting the number of mixtures arbitrarily as an input parameter, was extended with the algorithm that automatically adapts the model to statistical data. The probabilistic model was obtained by matching to the histogram of transition times between a particular pair of cameras. The proposed matching procedure uses a modified particle swarm optimization (mPSO). A way of using models of transition time in object re-identification is also presented. Experiments with the proposed method of modeling the transition time were carried out, and a comparison between previous and novel approach results are also presented, revealing that added swarms approximate normalized histograms very effectively. Moreover, the proposed swarm-based algorithm allows for modelling the same statistical data with a lower number of summands in GMM.

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Informacje szczegółowe

Kategoria:
Publikacja w czasopiśmie
Typ:
artykuły w czasopismach
Opublikowano w:
Electronics nr 10,
ISSN: 2079-9292
Język:
angielski
Rok wydania:
2021
Opis bibliograficzny:
Lisowski K., Czyżewski A.: Adaptive Method for Modeling of Temporal Dependencies between Fields of Vision in Multi-Camera Surveillance Systems// Electronics -,iss. 10(11) (2021), s.2-21
DOI:
Cyfrowy identyfikator dokumentu elektronicznego (otwiera się w nowej karcie) 10.3390/electronics10111303
Źródła finansowania:
  • Działalność statutowa/subwencja
Weryfikacja:
Politechnika Gdańska

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