Analysis of Crowded Scenes in Video

Mikel Rodriguez 1, 2 Josef Sivic 1, 2 Ivan Laptev 1, 2
2 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR8548
Abstract : In this chapter we first review the recent studies that have begun to address the various challenges associated with the analysis of crowded scenes. Next, we describe our two recent contributions to crowd analysis in video. First, we present a crowd analysis algorithm powered by prior behaviors that are learned on a large database of crowd videos gathered from the Internet. The proposed algorithm performs like state-of-the-art methods for tracking people having common crowd behaviors and outperforms the methods when the tracked individuals behave in an unusual way. Second, we address the problem of detecting and tracking a person in crowded video scenes. We formulate person detection as the optimization of a joint energy function combining crowd density estimation and the localization of individual people. The proposed methods are validated on a challenging video dataset of crowded scenes. Finally, the chapter concludes by describing ongoing and future research directions in crowd analysis.
Type de document :
Chapitre d'ouvrage
Jean-Yves Dufour. Intelligent Video Surveillance Systems, Wiley, pp.251-272, 2013
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https://hal.inria.fr/hal-01053878
Contributeur : Josef Sivic <>
Soumis le : dimanche 3 août 2014 - 18:35:52
Dernière modification le : jeudi 11 janvier 2018 - 06:23:05

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  • HAL Id : hal-01053878, version 1

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Mikel Rodriguez, Josef Sivic, Ivan Laptev. Analysis of Crowded Scenes in Video. Jean-Yves Dufour. Intelligent Video Surveillance Systems, Wiley, pp.251-272, 2013. 〈hal-01053878〉

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