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Proceedings Paper

Multiple hypothesis shape tracking using particle filtering and Hough-based observation models
Author(s): Alessio Dore; Majid Asadi; Carlo S. Regazzoni
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Paper Abstract

In the last years, the Particle Filter algorithm has been extensively proposed and employed for handling the problem of visual tracking of multiple moving objects under different assumptions. This wide usage is due to the capability of performing a recursive multiple hypothesis state estimation for non-linear non-Gaussian motion and observation models. In this paper a method, based on the Particle Filter framework, is proposed for multiple objects tracking, exploiting a target representation consisting of position and shape described as a fixed dimensionality vector composed by a fixed number of grouped target corners. However, usually, application domains of visual tracking algorithms are characterized by non-rigid objects and high occlusions rate entailing new corners to appear and others to disappear at each frame. In order to cope with this problem, a voting method (i.e. the Generalized Hough Transform) is employed to estimate the likelihood function to weight different propagated particles (i.e. multiple corners configurations describing shapes) by means of the corners extracted from the currently observed image. This method, in addition to the high dimensionality of the state representation, depicts the two main particularities of the presented Particle Filter. The proposed algorithm has been tested in a real-world domain and experiments indicate good results in tracking both rigid and non-rigid objects.

Paper Details

Date Published: 29 January 2007
PDF: 11 pages
Proc. SPIE 6508, Visual Communications and Image Processing 2007, 65080M (29 January 2007); doi: 10.1117/12.714112
Show Author Affiliations
Alessio Dore, Univ. degli Studi di Genova (Italy)
Majid Asadi, Univ. degli Studi di Genova (Italy)
Carlo S. Regazzoni, Univ. degli Studi di Genova (Italy)


Published in SPIE Proceedings Vol. 6508:
Visual Communications and Image Processing 2007
Chang Wen Chen; Dan Schonfeld; Jiebo Luo, Editor(s)

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