Computer Vision
Signal Processing
Machine learning
Robotics
Computational Neuroscience

Research Interests

Sparse approximation
Compressed-sensing
Inverse problems
Real-time vision
Bio-inspired vision
Large-scale vision
Big Visual data

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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