Adaptive hybrid likelihood model for visual tracking based on Gaussian particle filter

Yong Wang; Yihua Tan; Jinwen Tian

doi:10.1117/1.3465563

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1 July 2010 Adaptive hybrid likelihood model for visual tracking based on Gaussian particle filter

Yong Wang, Yihua Tan, Jinwen Tian

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Optical Engineering, 49(7), 077004 (2010). https://doi.org/10.1117/1.3465563

Abstract

We present a new scheme based on multiple-cue integration for visual tracking within a Gaussian particle filter framework. The proposed method integrates the color, shape, and texture cues of an object to construct a hybrid likelihood model. During the measurement step, the likelihood model can be switched adaptively according to environmental changes, which improves the object representation to deal with the complex disturbances, such as appearance changes, partial occlusions, and significant clutter. Moreover, the confidence weights of the cues are adjusted online through the estimation using a particle filter, which ensures the tracking accuracy and reliability. Experiments are conducted on several real video sequences, and the results demonstrate that the proposed method can effectively track objects in complex scenarios. Compared with previous similar approaches through some quantitative and qualitative evaluations, the proposed method performs better in terms of tracking robustness and precision.

©(2010) Society of Photo-Optical Instrumentation Engineers (SPIE)

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Yong Wang, Yihua Tan, and Jinwen Tian "Adaptive hybrid likelihood model for visual tracking based on Gaussian particle filter," Optical Engineering 49(7), 077004 (1 July 2010). https://doi.org/10.1117/1.3465563

Published: 1 July 2010

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