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2008
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SVD based Kalman particle filter for robust visual tracking

13 years 26 days ago
SVD based Kalman particle filter for robust visual tracking
Object tracking is one of the most important tasks in computer vision. The unscented particle filter algorithm has been extensively used to tackle this problem and achieved a great success, because it uses the UKF (unscented Kalman filter) to generate a sophisticated proposal distributions which incorporates the newest observations into the state transition distribution and thus overcomes the sample impoverishment problem suffered by the particle filter. However, UKF often encounters the ill-conditioned problem when solving the square root of the covariance matrix in practice. In this paper, we propose a novel Kalman particle filter based on SVD (singular value decomposition), and apply it for visual tracking. Experimental results demonstrate that, compared with the particle filter and the unscented particle filter, the proposed algorithm is more robust in tracking performance.
Qingdi Wei, Weiming Hu, Xi Li, Xiaoqin Zhang, Yang
Added 05 Nov 2009
Updated 06 Nov 2009
Type Conference
Year 2008
Where ICPR
Authors Qingdi Wei, Weiming Hu, Xi Li, Xiaoqin Zhang, Yanguo Wang, Zixiang Zhao
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