1. Marked Point Processes for Crowd Counting
Weina Ge and Robert T. Collins Computer Science and Engineering Department, The Pennsylvania State University, USA
MOTIVATION
We consider a crowd scene as a realization of an MPP
Learning Intrinsic Shape Classes
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
A rectangular covering
A shape covering
Bayesian approach
MPP prior
Combined with likelihood 1
“Soft” mask
Binary mask
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
RESULTS
CAVIAR
Soccer
Total # People
3728
1258
Detection Rate
0.92
0.84
FP Rate
0.02
0.06
π(θi|pi)
π(wi, hi|pi)
Estimating Extrinsic Parameters
robust regression
determine location, scale, orientation
Original image
Foreground blobs
Blob orientation axes in a frame
Blob orientation axes of a sequence
Inliers found by RANSAC
Vertical vanishing point
determine body shape
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
dimensions
CONTRIBUTIONS
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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