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 For more info: http://vision.cse.psu.edu/projects/mpp/mpp.html