GIST Scholar
College of Artificial Intelligence
Department of Electrical Engineering and Computer Science
1. Journal Articles
Robust auxiliary particle filter with an adaptive appearance model for visual tracking
- Type
- Article
- Citation
- Lecture Notes in Computer Science, v.6494 LNCS, no.PART 3, pp.718 - 731
- Issued Date
- 2011-05
- Abstract
- The algorithm proposed in this paper is designed to solve two challenging issues in visual tracking: uncertainty in a dynamic motion model and severe object appearance change. To avoid filter drift due to inaccuracies in a dynamic motion model, a sliding window approach is applied to particle filtering by considering a recent set of observations with which internal auxiliary estimates are sequentially calculated, so that the level of uncertainty in the motion model is significantly reduced. With a new auxiliary particle filter, abrupt movements can be effectively handled with a light computational load. Another challenge, severe object appearance change, is adaptively overcome via a modified principal component analysis. By utilizing a recent set of observations, the spatiotemporal piecewise linear subspace of an appearance manifold is incrementally approximated. In addition, distraction in the filtering results is alleviated by using a layered sampling strategy to efficiently determine the best fit particle in the high-dimensional state space. Compared to existing algorithms, the proposed algorithm produces successful results, especially when difficulties are combined. © 2011 Springer-Verlag Berlin Heidelberg.
- Publisher
- Springer Verlag
- ISSN
- 0302-9743
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