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Rao-Blackwellized particle filter for Gaussian mixture models and application to visual tracking

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2 Author(s)
Jungho Kim ; Korea Advanced Institute of Science and Technology, 373-1 Guseong-dong Yuseong-gu Daejeon, Korea ; In So Kweon

One of the most important problems in visual tracking is how to incrementally update the appearance model because the appearance of a target object can be easily changed with time when the target is a deformable object or it is moving under varying illumination conditions. To solve these problems, we present a Rao-Blackwellized particle filter (RBPF)-based object tracking algorithm with the adaptive appearance model represented by a Gaussian mixture model (or a mixture of Gaussians model) because a single Gaussian reveals limita tions in modeling the target appearance when observations are corrupted by occlusion or the tracking error. We demonstrate the robustness of the proposed method using well-known databases, such as the CAVIAR and the PETS databases.

Published in:

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Date of Conference:

22-27 May 2011