Density-aware person detection and tracking in crowds

Mikel Rodriguez 1, 2 Ivan Laptev 1 Josef Sivic 1 Jean-Yves Audibert 1, 3, 4
1 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
3 IMAGINE [Marne-la-Vallée]
LIGM - Laboratoire d'Informatique Gaspard-Monge, CSTB - Centre Scientifique et Technique du Bâtiment, ENPC - École des Ponts ParisTech
Abstract : We address the problem of person detection and tracking in crowded video scenes. While the detection of individual objects has been improved significantly over the recent years, crowd scenes remain particularly challenging for the detection and tracking tasks due to heavy occlusions, high person densities and significant variation in people's appearance. To address these challenges, we propose to leverage information on the global structure of the scene and to resolve all detections jointly. In particular, we explore constraints imposed by the crowd density and formulate person detection as the optimization of a joint energy function combining crowd density estimation and the localization of individual people. We demonstrate how the optimization of such an energy function significantly improves person detection and tracking in crowds. We validate our approach on a challenging video dataset of crowded scenes.
Type de document :
Communication dans un congrès
ICCV 2011 - 13th International Conference on Computer Vision, Nov 2011, Barcelona, Spain. 8 p., 2011
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Mikel Rodriguez, Ivan Laptev, Josef Sivic, Jean-Yves Audibert. Density-aware person detection and tracking in crowds. ICCV 2011 - 13th International Conference on Computer Vision, Nov 2011, Barcelona, Spain. 8 p., 2011. 〈hal-00654266〉

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