CNRS - Centre National de la Recherche Scientifique : UMR8548, Inria Paris-Rocquencourt, DI-ENS - Département d'informatique de l'École normale supérieure
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.
https://hal-enpc.archives-ouvertes.fr/hal-00654266
Contributeur : Jean-Yves Audibert
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Soumis le : jeudi 22 décembre 2011 - 10:21:55
Dernière modification le : jeudi 7 février 2019 - 15:49:23
Document(s) archivé(s) le : dimanche 4 décembre 2016 - 20:56:37
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. ⟨hal-00654266⟩
