“people localization”

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.