@inproceedings{abbd209e7cbd4f0eaca7a261dc381608,
title = "Marked point processes for crowd counting",
abstract = "A Bayesian marked point process (MPP) model is developed to detect and count people in crowded scenes. The model couples a spatial stochastic process governing number and placement of individuals with a conditional mark process for selecting body shape. We automatically learn the mark (shape) process from training video by estimating a mixture of Bernoulli shape prototypes along with an extrinsic shape distribution describing the orientation and scaling of these shapes for any given image location. The reversible jump Markov Chain Monte Carlo framework is used to efficiently search for the maximum a posteriori configuration of shapes, leading to an estimate of the count, location and pose of each person in the scene. Quantitative results of crowd counting are presented for two publiclyavailable datasets with known ground truth.",
author = "Weina Ge and Collins, {Robert T.}",
year = "2009",
doi = "10.1109/CVPRW.2009.5206621",
language = "English (US)",
isbn = "9781424439935",
series = "2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009",
publisher = "IEEE Computer Society",
pages = "2913--2920",
booktitle = "2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009",
address = "United States",
note = "2009 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009 ; Conference date: 20-06-2009 Through 25-06-2009",
}