“sparse signal representation”

Compressed Sensing: When sparsity meets sampling

Remark: Chapter of the book “Optical and Digital Image Processing - Fundamentals and Applications”, Edited by G. Cristòbal; P. Schelkens; H. Thienpont. Wiley-VCH, April 2011. ISBN:978-3-527-40956-3. (website of the book)

Sparsity-driven people localization algorithm: evaluation in crowded scenes environments

Abstract: We propose to evaluate our sparsity driven people localization framework on crowded complex scenes. The problem is recast as a linear inverse problem. It relies on deducing an occupancy vector, i.

Sport players detection and tracking with a mixed network of planar and omnidirectional cameras

Abstract: A generic approach is presented to detect and track people with a network of fixed and omnidirectional cameras given severely degraded foreground silhouettes. The problem is formulated as a sparsity constrained inverse problem.