NDLI: A Backmapping Approach for Graph-Based Object Tracking

Content Provider	IEEE Xplore Digital Library
Author	Paixao, T.M. Graciano, A. Cesar, R.M. Hirata, R.
Copyright Year	2008
Description	Author affiliation: Inst. of Math. & Stat., Univ. of Sao Paulo, Sao Paulo (Paixao, T.M.; Graciano, A.; Cesar, R.M.; Hirata, R.)
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.
Starting Page	45
Ending Page	52
File Size	10048207
Page Count	8
File Format	PDF
ISBN	9780769533582
ISSN	15301834
DOI	10.1109/SIBGRAPI.2008.32
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-12
Publisher Place	Brazil
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation model Image segmentation Computational modeling Target tracking Gallium Motion segmentation Analytical models Object Tracking Structural Pattern Recognition Attributed Relational Graph
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
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