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|Title:||Enhanced importance sampling: Unscented auxiliary particle filtering for visual tracking|
Van Den Hengel, A.
|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 Place:||Berlin, Germany|
|Series/Report no.:||Lecture notes in computer science ; 3339.|
|Conference Name:||Australian Joint Conference on Artificial Intelligence (17th : 2004 : Cairns, Qld.)|
|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|
|Appears in Collections:||Computer Science publications|
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