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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
CNRS - Centre National de la Recherche Scientifique : UMR8548, Inria Paris-Rocquencourt, DI-ENS - Département d'informatique de l'École normale supérieure
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.
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Contributor : Josef Sivic <>
Submitted on : Sunday, August 3, 2014 - 6:35:52 PM
Last modification on : Tuesday, May 4, 2021 - 2:06:02 PM


  • HAL Id : hal-01053878, version 1



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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