Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/29538
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Type: Conference paper
Title: Enhanced importance sampling: Unscented auxiliary particle filtering for visual tracking
Author: Shen, C.
Van Den Hengel, A.
Dick, A.
Brooks, M.
Citation: AI 2004 : advances in artificial intelligence : 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004 : proceedings / Geoffrey I. Webb, Xinghuo Yu (eds.), pp. 180-191
Publisher: Springer
Publisher Place: Berlin, Germany
Issue Date: 2004
Series/Report no.: Lecture notes in computer science ; 3339.
ISBN: 3540240594
ISSN: 0302-9743
1611-3349
Conference Name: Australian Joint Conference on Artificial Intelligence (17th : 2004 : Cairns, Qld.)
Statement of
Responsibility: 
Chunhua Shen, Anton van den Hengel, Anthony Dick and Michael J. Brooks
Abstract: The particle filter has attracted considerable attention in visual tracking due to its relaxation of the linear and Gaussian restrictions in the state space model. It is thus more flexible than the Kalman filter. However, the conventional particle filter uses system transition as the proposal distribution, leading to poor sampling efficiency and poor performance in visual tracking. It is not a trivial task to design satisfactory proposal distributions for the particle filter. In this paper, we introduce an improved particle filtering framework into visual tracking, which combines the unscented Kalman filter and the auxiliary particle filter. The efficient unscented auxiliary particle filter (UAPF) uses the unscented transformation to predict one-step ahead likelihood and produces more reasonable proposal distributions, thus reducing the number of particles required and substantially improving the tracking performance. Experiments on real video sequences demonstrate that the UAPF is computationally efficient and outperforms the conventional particle filter and the auxiliary particle filter.
Description: The original publication is available at www.springerlink.com
RMID: 0020041884
DOI: 10.1007/b104336
Published version: http://www.springerlink.com/content/3jyxx8h6exjwuygk/
Appears in Collections:Computer Science publications

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