Thumbnail
Access Restriction
Subscribed

Author Paixao, T.M. ♦ Graciano, A. ♦ Cesar, R.M. ♦ Hirata, R.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2008
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods
Subject Keyword Adaptation model ♦ Image segmentation ♦ Computational modeling ♦ Target tracking ♦ Gallium ♦ Motion segmentation ♦ Analytical models ♦ Object Tracking ♦ Structural Pattern Recognition ♦ Attributed Relational Graph
Abstract Model-based methods play a central role to solve different problems in computer vision. A particular important class of such methods rely on graph models where an object is decomposed into a number of parts, each one being represented by a graph vertex. A graph model-based tracking algorithm has been recently introduced in which a model is generated for a given frame (reference frame) and used to track a target object in the subsequent ones. Because the view of an object changes along the video sequence, the solution updated the model using affine transformations. This paper proposes a different approach and improves the previous one in several ways. Firstly, instead of updating the model, each analyzed frame is backmapped to the model space, thus providing more robustness to the method because model parameters do not have to be modified. A different method for model generation based on user traces has also been implemented and used. This model generation approach is much simpler and user-friendly. Finally, a graph-matching algorithm that has been recently proposed is used for object tracking. This new algorithm is more efficient and leads to better matching results. Experimental results using synthetic and real sequences from the CAVIAR project are shown and discussed.
Description Author affiliation: Inst. of Math. & Stat., Univ. of Sao Paulo, Sao Paulo (Paixao, T.M.; Graciano, A.; Cesar, R.M.; Hirata, R.)
ISBN 9780769533582
ISSN 15301834
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2008-10-12
Publisher Place Brazil
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 9.58 MB
Page Count 8
Starting Page 45
Ending Page 52


Source: IEEE Xplore Digital Library