A sparsity constrained inverse problem to locate people in a network of cameras

Authors:
Alexandre Alahi;Yannick Boursier;Laurent Jacques;Pierre Vandergheynst
Affiliations:
Institute of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;Institute of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland;Institute of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland and Communications and Remote Sensing Laboratory, Université catholique d ...;Institute of Electrical Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
Venue:
DSP'09 Proceedings of the 16th international conference on Digital Signal Processing
Year:
2009

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Abstract

A novel approach is presented to locate dense crowd of people in a network of fixed cameras given the severely degraded background subtracted silhouettes. The problem is formulated as a sparsity constrained inverse problem using an adaptive dictionary constructed on-line. The framework has no constraint on the number of cameras neither on the surface to be monitored. Even with a single camera. partially occluded and grouped people are correctly detected and segmented. Qualitative results are presented in indoor and outdoor scenes.