Object Tracking Using Probabilistic Principal Component Analysis Based on Particle Filtering Framework

Abstract:

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In this paper, an object tracking approach is introduced for color video sequences. The approach presents the integration of color distributions and probabilistic principal component analysis (PPCA) into particle filtering framework. Color distributions are robust to partial occlusion, are rotation and scale invariant and are calculated efficiently. Principal Component Analysis (PCA) is used to update the eigenbasis and the mean, which can reflect the appearance changes of the tracked object. And a low dimensional subspace representation of PPCA efficiently adapts to these changes of appearance of the target object. At the same time, a forgetting factor is incorporated into the updating process, which can be used to economize on processing time and enhance the efficiency of object tracking. Computer simulation experiments demonstrate the effectiveness and the robustness of the proposed tracking algorithm when the target object undergoes pose and scale changes, defilade and complex background.

Info:

Periodical:

Advanced Materials Research (Volumes 341-342)

Edited by:

Liu Guiping

Pages:

790-797

DOI:

10.4028/www.scientific.net/AMR.341-342.790

Citation:

Z. Y. Xiang et al., "Object Tracking Using Probabilistic Principal Component Analysis Based on Particle Filtering Framework", Advanced Materials Research, Vols. 341-342, pp. 790-797, 2012

Online since:

September 2011

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Price:

$35.00

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