The GMCPHD tracker applied to the Clutter09 dataset

Author

Georgescu, Ramona ; Willett, P.

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Connecticut, Storrs, CT, USA

fYear

2013

fDate

9-12 July 2013

Firstpage

530

Lastpage

537

Abstract

The contribution of this paper is twofold: first, it exposes the tracking community to a dataset previously used in acoustics studies and second, it explores the use of the features in this real dataset in clutter removal. For the latter, the Minimal Redundancy Maximal Relevance (MRMR) technique was chosen for feature selection due to its flexibility on big data; the top features ranked by MRMR are sent to a C4.5 decision tree for classification. Contacts that were not identified as clutter are given to the Gaussian Mixture Cardinalized Probability Hypothesis Density (GMCPHD) tracker. Several metrics show that a very small number of features can be employed for satisfactory tracking performance.

Keywords

Gaussian processes; clutter; decision trees; feature extraction; probability; signal classification; sonar signal processing; sonar tracking; C4.5 decision tree; Clutter09 dataset; GMCPHD tracker; Gaussian mixture cardinalized probability hypothesis density tracker; MRMR technique; acoustics study; classification; clutter removal; feature selection; minimal redundancy maximal relevance technique; sonar; tracking performance; Arrays; Clutter; Delays; Feature extraction; Signal to noise ratio; Sonar; Target tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion (FUSION), 2013 16th International Conference on

Conference_Location

Istanbul

Print_ISBN

978-605-86311-1-3

Type

conf

Filename

6641326