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Evaluation of Our Sparsity-based People Localization Algorithm in Crowded Scenes

We evaluate our sparsity-driven people localization framework on crowded complex scene. The problem is recast as a linear inverse problem. It relies on deducing an occupancy vector, i.e. the discretized occupancy of people on the ground, from the noisy binary silhouettes observed as foreground pixels in each camera. This inverse problem is regularized by imposing a sparse occupancy vector, i.e. made of few non-zero elements, while a particular dictionary of silhouettes linearly maps these non-empty grid locations to the multiple silhouettes viewed by the cameras network. This constitutes a linearization of the problem, where non-linearities, such as occlusions, are treated as additional noise on the observed silhouettes. Mathematically, we express the final inverse problem either as a Lasso convex optimization programs. The sparsity measure is reinforced by iteratively re-weighting the l1-norm of the occupancy vector for better approximating its l0 ``norm'', and a ``repulsive'' sparsity is used to adapt further the Lasso procedure to the occupancy reconstruction. Practically, an adaptive sampling process is proposed to reduce the computation cost and monitor a large occupancy area. (more detail)

Qualitative results are presented on a the PETS 2009 dataset. The proposed algorithm detects people in high density crowd, count and track them given severely degraded extracted foreground silhouettes (white contours).

Some examples:

Locating in high density crowds:

Detecting and tracking in crowds:

Detecting and tracking in sparse crowds:

 

Related publication:

A. Alahi, L. Jacques, Y. Boursier and P. Vandergheynst, Sparsity-driven People Localization Algorithm: Evaluation in Crowded Scenes Environments , accepted to IEEE International Workshop on Performance Evaluation of Tracking and Surveillance , 2009.
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