Random Infinite Tree and Dependent Poisson Diffusion Process for Nonparametric Bayesian Modeling in Multiple Object Tracking

Bahman Moraffah; Antonia Papandreou-Suppappola

doi:10.1109/ICASSP.2019.8682370

Random Infinite Tree and Dependent Poisson Diffusion Process for Nonparametric Bayesian Modeling in Multiple Object Tracking

Bahman Moraffah, Antonia Papandreou-Suppappola

Engineering, Ira A. Fulton Schools of (IAFSE)

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

Abstract

Recent methods for tracking multiple objects have addressed important issues such as time-varying cardinality, unordered sets of measurements, and object labeling. Another challenge is how to robustly associate objects on a new scene with previously estimated objects. We propose a new method to track a dynamically varying number of objects using information from previously tracked ones. Our method is based on nonparametric Bayesian modeling using diffusion processes and random trees. We use simulations to demonstrate the performance of the proposed algorithm and compare it to a labeled multi-Bernoulli filter based tracker.

Original language	English (US)
Title of host publication	2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5217-5221
Number of pages	5
ISBN (Electronic)	9781479981311
DOIs	https://doi.org/10.1109/ICASSP.2019.8682370
State	Published - May 2019
Event	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom Duration: May 12 2019 → May 17 2019

Publication series

Name	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2019-May
ISSN (Print)	1520-6149

Conference

Conference	44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
Country/Territory	United Kingdom
City	Brighton
Period	5/12/19 → 5/17/19

Keywords

Dirichlet diffusion tree
Multiple object tracking
nonparametric Bayesian modeling
random tree
sequential Monte Carlo method

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

Access to Document

10.1109/ICASSP.2019.8682370

Cite this

Moraffah, B., & Papandreou-Suppappola, A. (2019). Random Infinite Tree and Dependent Poisson Diffusion Process for Nonparametric Bayesian Modeling in Multiple Object Tracking. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 5217-5221). Article 8682370 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8682370

Random Infinite Tree and Dependent Poisson Diffusion Process for Nonparametric Bayesian Modeling in Multiple Object Tracking. / Moraffah, Bahman; Papandreou-Suppappola, Antonia.
2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. p. 5217-5221 8682370 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Moraffah, B & Papandreou-Suppappola, A 2019, Random Infinite Tree and Dependent Poisson Diffusion Process for Nonparametric Bayesian Modeling in Multiple Object Tracking. in 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings., 8682370, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, vol. 2019-May, Institute of Electrical and Electronics Engineers Inc., pp. 5217-5221, 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019, Brighton, United Kingdom, 5/12/19. https://doi.org/10.1109/ICASSP.2019.8682370

Moraffah B, Papandreou-Suppappola A. Random Infinite Tree and Dependent Poisson Diffusion Process for Nonparametric Bayesian Modeling in Multiple Object Tracking. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2019. p. 5217-5221. 8682370. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2019.8682370

Moraffah, Bahman ; Papandreou-Suppappola, Antonia. / Random Infinite Tree and Dependent Poisson Diffusion Process for Nonparametric Bayesian Modeling in Multiple Object Tracking. 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 5217-5221 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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abstract = "Recent methods for tracking multiple objects have addressed important issues such as time-varying cardinality, unordered sets of measurements, and object labeling. Another challenge is how to robustly associate objects on a new scene with previously estimated objects. We propose a new method to track a dynamically varying number of objects using information from previously tracked ones. Our method is based on nonparametric Bayesian modeling using diffusion processes and random trees. We use simulations to demonstrate the performance of the proposed algorithm and compare it to a labeled multi-Bernoulli filter based tracker.",

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Random Infinite Tree and Dependent Poisson Diffusion Process for Nonparametric Bayesian Modeling in Multiple Object Tracking

Abstract

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Keywords

ASJC Scopus subject areas

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