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 templates 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 publicly available datasets for which ground truth is known.

Citation

Weina Ge and Robert T. Collins. Marked Point Processes for Crowd Counting, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 2009. [PDF][Bibtex][Supplementary derivations for the intrinsic shape model]

Poster

Download the poster presented at CVPR 2009: [PNG 4.5M].

Results

	video illustration of counting for the CAVIAR sequence [Download 17.2M]
	video illustration of counting for the soccer sequence [[Download 6.87M]
	video illustration of the RJMCMC iterations with birth, death, and update proposals for people counting [Download 3.64M]

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