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Enhanced laplacian group sparse learning with lifespan outlier rejection for visual tracking

Bozorgtabar, Behzad; Goecke, Roland


Recently, sparse based learning methods have attracted much attention in robust visual tracking due to their effectiveness and promising tracking results. By representing the target object sparsely, utilising only a few adaptive dictionary templates, in this paper, we introduce a new particle filter based tracking method, in which we aim to capture the underlying structure among the particle samples using the proposed similarity graph in a Laplacian group sparse framework, such that the...[Show more]

CollectionsANU Research Publications
Date published: 2015
Type: Conference paper
Source: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
DOI: 10.1007/978-3-319-16814-2_37


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