Object Tracking by introducing Stochastic Filtering into Window-Matching Techniques

Author

Vidal, Flávio B. ; Alcalde, Víctor H Casanova

Author_Institution

Univ. of Brasilia, Brasilia

fYear

2007

fDate

20-23 June 2007

Firstpage

31

Lastpage

36

Abstract

This paper describes the development and the application of an object tracking algorithm from a sequence of images. The algorithm is based on window-matching techniques using the sum of squared differences (SSD) as a distance-similarity measure, but adding stochastic filtering. The algorithm is then applied for tracking a vehicle on an urban environment and for tracking the ball on a ping-pong game. It is concluded that incorporating the Kalman filtering greatly improves the tracking performance.

Keywords

Kalman filters; image matching; image sequences; object detection; target tracking; Kalman filtering; distance similarity measure; image sequences; object tracking; ping pong game; stochastic filtering; sum of squared differences; urban environment; window matching technique; Biomedical measurements; Computational intelligence; Digital images; Filtering algorithms; Kalman filters; Motion detection; Robotics and automation; Stochastic processes; USA Councils; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Robotics and Automation, 2007. CIRA 2007. International Symposium on

Conference_Location

Jacksonville, FI

Print_ISBN

1-4244-0790-7

Electronic_ISBN

1-4244-0790-7

Type

conf

DOI

10.1109/CIRA.2007.382869

Filename

4269869