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Marked Point Processes for Crowd Counting

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Learning Intrinsic Shape Classes. Bayesian approach. determine location, scale, orientation. Estimating Extrinsic Parameters. Original image. Foreground blobs. Blob orientation axes in a frame. determine body shape. Blob orientation axes of a sequence. Inliers found by RANSAC.

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Learning Intrinsic Shape Classes
  • Bayesian approach

determine location, scale, orientation

Estimating Extrinsic Parameters

Original image

Foreground blobs

Blob orientation axes in a frame

determine body shape

Blob orientation axes of a sequence

Inliers found by RANSAC

Vertical vanishing point

Binary mask

“Soft” mask

A rectangular covering

A shape covering

MPP prior

Combined with likelihood

Marked Point Processes for Crowd Counting

Weina Ge and Robert T. CollinsComputer Science and Engineering Department, The Pennsylvania State University, USA


We consider a crowd scene as a realization of an MPP

  • Delineate pedestrians in a fg mask using shape coverings
  • Model the shapes using a mixture of Bernoulli distributions
  • Automatically determine the number of mixture components by Bayesian EM

EM iterations

Bayesian EM with Dirichlet prior

  • Adapt to different videos by learning the shape models

Training samples

Automatically learned shapes

  • Our MPP combines a stochastic point process with a conditional mark process to model prior knowledge on the spatial distribution of an unknown number of pedestrians



π(wi, hi|pi)

robust regression

  • Estimating the MPP Configurations by RJMCMC

RJMCMC: stochastic mode seeking procedure with two different types of proposals

1. update shape/location

local updates to a current configuration

2. birth/death

jumps between two configurations of different


  • An MPP with a conditional mark process to model known correlations between bounding box size/orientation and image location
  • Bayesian EM for automatic learning of Bernoulli shapes

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