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dc.contributor.authorKim, Du Yong
dc.contributor.authorYang, E.
dc.contributor.authorJeon, M.
dc.contributor.authorShin, V.
dc.date.accessioned2017-08-24T02:18:19Z
dc.date.available2017-08-24T02:18:19Z
dc.date.created2017-08-23T07:21:48Z
dc.date.issued2011
dc.date.submitted2017-08-23
dc.identifier.citationKim, 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.urihttp://hdl.handle.net/20.500.11937/55342
dc.identifier.doi10.1007/978-3-642-19318-7_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.titleRobust auxiliary particle filter with an adaptive appearance model for visual tracking
dc.typeConference Paper
dcterms.dateSubmitted2017-08-23
dcterms.source.volume6494 LNCS
dcterms.source.startPage718
dcterms.source.endPage731
dcterms.source.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dcterms.source.seriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dcterms.source.isbn9783642193170
curtin.digitool.pid255257
curtin.departmentDepartment of Electrical and Computer Engineering
curtin.identifier.elementsidELEMENTS-211615
curtin.accessStatusFulltext not available


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