@inproceedings{d7e40741b1b64779aba8ea502d65a189,
title = "Statistically representative cloud of particles for crowd flow tracking",
abstract = "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.",
keywords = "Crowd, Entry-exit areas detection, Entry-exit areas linkage, Flow tracking, Occlusions, Particle video",
author = "Patrick Jamet and Chew, \{Stephen Chai Kheh\} and Antoine Fagette and Dufour, \{Jean Yves\} and Daniel Racoceanu",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 3rd International Conference on Pattern Recognition Applications and Methods, ICPRAM 2014 ; Conference date: 06-03-2014 Through 08-03-2014",
year = "2015",
doi = "10.1007/978-3-319-25530-9\_16",
language = "English",
isbn = "9783319255293",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "237--251",
editor = "\{de Marsico\}, Maria and Ana Fred and Antoine Tabbone",
booktitle = "Pattern Recognition Applications and Methods - 3rs International Conference, ICPRAM 2014, Revised Selected Papers",
}