Enhanced importance sampling: unscented auxiliary particle filtering for visual tracking

Authors:
Chunhua Shen;Anton van den Hengel;Anthony Dick;Michael J. Brooks
Affiliations:
School of Computer Science, The University of Adelaide, Adelaide, SA, Australia;School of Computer Science, The University of Adelaide, Adelaide, SA, Australia;School of Computer Science, The University of Adelaide, Adelaide, SA, Australia;School of Computer Science, The University of Adelaide, Adelaide, SA, Australia
Venue:
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Year:
2004

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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.