Tracking Multiple Objects with Multimodal Dependent Measurements: Bayesian Nonparametric Modeling

Bahman Moraffah, Cesar Brito, Bindya Venkatesh, Antonia Papandreou-Suppappola

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We address the problem of multi-sensor multi-object tracking with unknown state parameter information. We develop algorithms capable of dealing with unknown time-dependent object and measurement cardinality and object identities. Additionally, given the dependent observations from sensors, our model takes advantage of the additional information to improve tracking performance. We robustly estimate the evolving objects and measurement cardinality with use of nonparametric modeling. In particular, we employ a dependent Dirichlet process to provide a prior on the time-varying object state distributions, and a hierarchical Dirichlet process (HDP) mixture to model measurement dependency. We demonstrate through simulations that providing multimodal dependent measurements the proposed method can accurately estimate the trajectory of objects and robustly determine the time-dependent cardinality. We also compare this method to the dependent Dirichlet process evolutionary Markov modeling (DDP-EMM) and demonstrate its improved tracking performance.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1847-1851
Number of pages5
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Externally publishedYes
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
CountryUnited States
CityPacific Grove
Period11/3/1911/6/19

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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  • Cite this

    Moraffah, B., Brito, C., Venkatesh, B., & Papandreou-Suppappola, A. (2019). Tracking Multiple Objects with Multimodal Dependent Measurements: Bayesian Nonparametric Modeling. In M. B. Matthews (Ed.), Conference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 (pp. 1847-1851). [9048817] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2019-November). IEEE Computer Society. https://doi.org/10.1109/IEEECONF44664.2019.9048817