Comparing a Kalman Filter and a Particle Filter in a Multiple Objects Tracking Application

Author

Marrón, M. ; García, J.C. ; Sotelo, M.A. ; Cabello, M. ; Pizarro, D. ; Huerta, F. ; Cerro, J.

Author_Institution

Alcala Univ., Madrid

fYear

2007

fDate

3-5 Oct. 2007

Firstpage

1

Lastpage

6

Abstract

Two of the most important solutions in position estimation are compared, in this paper, in order to test their efficiency in a multi-tracking application in an unstructured and complex environment. A particle filter is extended and adapted with a clustering process in order to track a variable number of objects. The other approach is to use a Kalman filter with an association algorithm for each of the objects to track. Both algorithms are described in the paper and the results obtained with their real-time execution in the mentioned application are shown. Finally interesting conclusions extracted from this comparison are remarked at the end.

Keywords

Kalman filters; maximum likelihood estimation; object detection; particle filtering (numerical methods); pattern clustering; robot vision; stereo image processing; target tracking; Kalman filter; association algorithm; clustering process; complex environment; intelligent vehicle; multiple object tracking; multitracking application; particle filter; position estimation; robotics; stereo vision; unstructured environment; Algorithm design and analysis; Clustering algorithms; Data mining; Electronic equipment testing; Particle filters; Particle tracking; Position measurement; Robots; State estimation; Yttrium; position estimation; probabilistic algorithms; robotics; tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Signal Processing, 2007. WISP 2007. IEEE International Symposium on

Conference_Location

Alcala de Henares

Print_ISBN

978-1-4244-0829-0

Electronic_ISBN

978-1-4244-0830-6

Type

conf

DOI

10.1109/WISP.2007.4447520

Filename

4447520