A simple and efficient template matching algorithm
- 7 July 2001
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 2, 544-549
- https://doi.org/10.1109/iccv.2001.937673
Abstract
We propose a general framework for object tracking in video images. It consists of low-order parametric models for the image motion of a target region. These models are used to predict the movement and to track the target. The difference of intensity between the pixels belonging to the current region and the pixels of the selected target (learnt during an off-line stage) allows a straightforward prediction of the region position in the current image. The proposed algorithm allows to track in real time (less than 10 ms) any planar textured target under homographic motions. This algorithm is very simple (a few lines of code) and very efficient (less than 10 ms on a 150 MHz hardware).Keywords
This publication has 12 references indexed in Scilit:
- Real time tracking and modeling of faces: an EKF-based analysis by synthesis approachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Solution of the Simultaneous Pose and Correspondence Problem Using Gaussian Error ModelComputer Vision and Image Understanding, 1999
- Efficient region tracking with parametric models of geometry and illuminationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1998
- Matching Feature Points in Image Sequences through a Region-Based MethodComputer Vision and Image Understanding, 1997
- 3D Pose Estimation by Directly Matching Polyhedral Models to Gray Value GradientsInternational Journal of Computer Vision, 1997
- Task-specific gesture analysis in real-time using interpolated viewsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1996
- Visual learning and recognition of 3-d objects from appearanceInternational Journal of Computer Vision, 1995
- Robust model-based motion tracking through the integration of search and estimationInternational Journal of Computer Vision, 1992
- Eigenfaces for RecognitionJournal of Cognitive Neuroscience, 1991
- Recovery of the 3-D location and motion of a rigid object through camera image (An Extended Kalman Filter Approach)International Journal of Computer Vision, 1989