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Compressive Evaluation in Human Motion Tracking

Lu, Yifan; Wang, Lei; Li, Hongdong; Hartley, Richard


The powerful theory of compressive sensing enables an efficient way to recover sparse or compressible signals from non-adaptive, sub-Nyquist-rate linear measurements. In particular, it has been shown that random projections can well approximate an isometry, provided that the number of linear measurements is no less than twice of the sparsity level of the signal. Inspired by these, we propose a compressive anneal particle filter to exploit sparsity existing in image-based human motion tracking....[Show more]

CollectionsANU Research Publications
Date published: 2010
Type: Conference paper
Source: Proceedings of ACCV 2010
DOI: 10.1007/978-3-642-19282-1_15


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