Motion detection and tracking using deformable templates

Author

Pérez, P. ; Gidas, B.

Author_Institution

Dept. of Appl. Math., Brown Univ., Providence, RI, USA

Volume

2

fYear

1994

fDate

13-16 Nov 1994

Firstpage

272

Abstract

We propose an object-based framework for detection and tracking of moving objects in a sequence of images. Two key ingredients of the approach are appropriate object models based on Grenander´s (see General Pattern Theory, 1993) deformable templates and spatio-temporal data models. Detection and tracking problems are formulated as optimization problems. Detection employs a Metropolis-type procedure starting from a random initial configuration, while tracking involves a deterministic nonlinear Gauss-Seidel algorithm. We present experimental results with real data on a highway traffic sequence

Keywords

Bayes methods; data structures; image sequences; iterative methods; motion estimation; optimisation; road traffic; tracking; Bayes framework; Metropolis-type procedure; deformable templates; deterministic nonlinear Gauss-Seidel algorithm; experimental results; highway traffic sequence; image sequence; motion detection; motion tracking; moving objects; object models; optimization problems; random initial configuration; real data; spatio-temporal data models; Cameras; Data models; Deformable models; Mathematics; Motion detection; Object detection; Object oriented modeling; Road transportation; Shape; Tracking;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference

Conference_Location

Austin, TX

Print_ISBN

0-8186-6952-7

Type

conf

DOI

10.1109/ICIP.1994.413574

Filename

413574