Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/75301
Title: Visual tracking by using kalman gradient vector flow (KGVF) snakes
Authors: Lam, THW 
Lee, RST 
Issue Date: 2004
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2004, v. 3214 LNCS, no. , p. 557-563 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: In this paper, we propose a new tracking model called Kalman Gradient Vector Flow (KGVF) snakes. KGVF snakes employ the Gradient Vector Flow (GVF) model [9] with the Kalman Filter [4] for object tracking. GVF model is an active contour model that used gradient vector flow field as the external force. This force ensures a larger capture range in the model and stronger ability to contract to the object boundary. Kalman Filter is an estimation algorithm that can be used in stochastic environment. In this paper, we explain how KGVF snakes works and also we have done some experiments to show how KGVF snakes have a strong tracking ability in clutter scenes.
Description: International Conference on Knowledge-Based and Intelligent Information and Engineering Systems [KES], 20-25 September 2004, Wellington, New Zealand
URI: http://hdl.handle.net/10397/75301
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-540-30133-2_73
Appears in Collections:Conference Paper

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