Evaluation of sampling-based pedestrian detection for crowd counting

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

With increases in computing power, the once computationally expensive sampling-based methods have been successfully applied to solve many hard vision problems of combinatorial nature, such as image segmentation, video tracking, and object detection. In this paper, we perform pedestrian detection using the reversible jump Markov Chain Monte Carlo (RJMCMC) sampling method. A crowd scene is viewed as a realization of a Marked Point Process (MPP) that consists of a random set of people in a bounded region. Each person is associated with a random 'mark' that governs their location and size in the image. To automatically infer the number of people in the scene and their spatial locations, RJMCMC is used to sample person hypotheses from an underlying stochastic process and evaluate them against the image observation to find the optimal configuration that best explains the image. We further extend the detector to hypothesize people not in the image plane but in 3D space, by incorporating multi-view information. The detection performance of both the single- and multi-view versions are evaluated on the crowd counting task in the PETS 2009 dataset.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009
DOIs
StatePublished - Dec 1 2009
Event12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009 - Snowbird, UT, United States
Duration: Dec 7 2009Dec 9 2009

Publication series

NameProceedings of the 12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009

Other

Other12th IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, PETS-Winter 2009
CountryUnited States
CitySnowbird, UT
Period12/7/0912/9/09

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Control and Systems Engineering

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