Statistically representative cloud of particles for crowd flow tracking

Patrick Jamet, Stephen Chai Kheh Chew, Antoine Fagette, Jean Yves Dufour, Daniel Racoceanu

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


This paper deal with the flow tracking topic applied to dense crowds of pedestrians. Using the estimated density, a cloud of particles is spread on the image and propagated according to the optical flow. Each particles embedding physical properties similar to those of a pedestrian, this cloud of particles is considered as statistically representative of the crowd. Therefore, the behavior of the particles can be validated with respect to the behavior expected from pedestrians and potentially optimized if needed. Three applications are derived by analysis of the cloud behavior: the detection of the entry and exit areas of the crowd in the image, the detection of dynamic occlusions and the possibility to link entry areas with exit ones according to the flow of the pedestrians. The validation is performed on synthetic data and shows promising results.

Idioma originalInglés
Título de la publicación alojadaPattern Recognition Applications and Methods - 3rs International Conference, ICPRAM 2014, Revised Selected Papers
EditoresMaria de Marsico, Ana Fred, Antoine Tabbone
EditorialSpringer Verlag
Número de páginas15
ISBN (versión impresa)9783319255293
EstadoPublicada - 2015
Publicado de forma externa
Evento3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014 - Angers, Francia
Duración: 6 mar. 20148 mar. 2014

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014


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