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People counting via multiple views using a fast information fusion approach

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Abstract

Real-time estimates of a crowd size is a central task in civilian surveillance. In this paper we present a novel system counting people in a crowd scene with overlapping cameras. This system fuses all single view foreground information to localize each person present on the scene. The purpose of our fusion strategy is to use the foreground pixels of each single views to improve real-time objects association between each camera of the network. The foreground pixels are obtained by using an algorithm based on codebook. In this work, we aggregate the resulting silhouettes over cameras network, and compute a planar homography projection of each camera’s visual hull into ground plane. The visual hull is obtained by finding the convex hull of the foreground pixels. After the projection into the ground plane, we fuse the obtained polygons by using the geometric properties of the scene and on the quality of each camera detection. We also suggest a region-based approach tracking strategy which keeps track of people movements and of their identities along time, also enabling tolerance to occasional misdetections. This tracking strategy is implemented on the result of the views fusion and allows to estimate the crowd size dependently on each frame. Assessment of experiments using public datasets proposed for the evaluation of counting people system demonstrates the performance of our fusion approach. These results prove that the fusion strategy can run in real-time and is efficient for making data association. We also prove that the combination of our fusion approach and the proposed tracking improve the people counting.

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Acknowledgments

This work is partially funded by the Association AS2V and Fondation Jacques De Rette, France. Authors are grateful to the Service de Coopération et d’Action Culturelle de l’Ambassade de France au Bénin. We also appreciate the valuable comments provided by the anonymous reviewers as these have improved the manuscript immensely.

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Authors and Affiliations

  1. EA 4491 - LISIC - Laboratoire d’Informatique Signal et Image de la Côte d’Opale, Université Littoral Côte d’Opale, 62228, Calais, France

    Mikaël A. Mousse & Cina Motamed

  2. Unité de Recherche en Informatique et Sciences Appliquées, Institut de Mathématiques et de Sciences Physiques, Université d’Abomey-Calavi, Abomey-Calavi, Bénin

    Mikaël A. Mousse & Eugène C. Ezin

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  1. Mikaël A. Mousse
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  2. Cina Motamed
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  3. Eugène C. Ezin
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Correspondence to Mikaël A. Mousse.

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Mousse, M.A., Motamed, C. & Ezin, E.C. People counting via multiple views using a fast information fusion approach. Multimed Tools Appl 76, 6801–6819 (2017). https://doi.org/10.1007/s11042-016-3352-z

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  • DOI: https://doi.org/10.1007/s11042-016-3352-z

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