Robust auxiliary particle filter with an adaptive appearance model for visual tracking
dc.contributor.author | Kim, Du Yong | |
dc.contributor.author | Yang, E. | |
dc.contributor.author | Jeon, M. | |
dc.contributor.author | Shin, V. | |
dc.date.accessioned | 2017-08-24T02:18:19Z | |
dc.date.available | 2017-08-24T02:18:19Z | |
dc.date.created | 2017-08-23T07:21:48Z | |
dc.date.issued | 2011 | |
dc.identifier.citation | Kim, D.Y. and Yang, E. and Jeon, M. and Shin, V. 2011. Robust auxiliary particle filter with an adaptive appearance model for visual tracking, pp. 718-731. | |
dc.identifier.uri | http://hdl.handle.net/20.500.11937/55342 | |
dc.identifier.doi | 10.1007/978-3-642-19318-7_56 | |
dc.description.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. | |
dc.title | Robust auxiliary particle filter with an adaptive appearance model for visual tracking | |
dc.type | Conference Paper | |
dcterms.source.volume | 6494 LNCS | |
dcterms.source.startPage | 718 | |
dcterms.source.endPage | 731 | |
dcterms.source.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dcterms.source.series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | |
dcterms.source.isbn | 9783642193170 | |
curtin.department | Department of Electrical and Computer Engineering | |
curtin.accessStatus | Fulltext not available |
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