Journal of Mathematical Imaging and Vision

, Volume 41, Issue 1, pp 39–58

Sparsity Driven People Localization with a Heterogeneous Network of Cameras

  • Alexandre Alahi
  • Laurent Jacques
  • Yannick Boursier
  • Pierre Vandergheynst
Article

DOI: 10.1007/s10851-010-0258-7

Cite this article as:
Alahi, A., Jacques, L., Boursier, Y. et al. J Math Imaging Vis (2011) 41: 39. doi:10.1007/s10851-010-0258-7

Abstract

This paper addresses the problem of localizing people in low and high density crowds with a network of heterogeneous cameras. The problem is recast as a linear inverse problem. It relies on deducing the discretized occupancy vector 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. The proposed framework is (i) generic to any scene of people, i.e., people are located in low and high density crowds, (ii) scalable to any number of cameras and already working with a single camera, (iii) unconstrained by the scene surface to be monitored, and (iv) versatile with respect to the camera’s geometry, e.g., planar or omnidirectional.

Qualitative and quantitative results are presented on the APIDIS and the PETS 2009 Benchmark datasets. The proposed algorithm successfully detects people occluding each other given severely degraded extracted features, while outperforming state-of-the-art people localization techniques.

Keywords

People detectionLocalizationSparse approximationConvex optimizationOmnidirectional camerasDictionaryMulti-view

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Alexandre Alahi
    • 1
  • Laurent Jacques
    • 2
  • Yannick Boursier
    • 3
  • Pierre Vandergheynst
    • 1
  1. 1.Signal Processing LabEPFLLausanneSwitzerland
  2. 2.Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM)University of LouvainLouvain-la-NeuveBelgium
  3. 3.Centre de Physiques des Particules de MarseilleAix-Marseille Université, CNRS/IN2P3MarseillesFrance