Real-time people counting using blob descriptor

Research output: Contribution to journalConference articlepeer-review

22 Citations (Scopus)

Abstract

We propose a system for counting the number of pedestrians in real-time. This system estimates "how many pedestrians are and where they are in video sequences" by the following procedures. First, candidate regions are segmented into blobs according to background subtraction. Second, a set of features are extracted from each blob and a neural network estimates the number of pedestrians corresponding to each set of features. To realize real-time processing, we used only simple and valid features, and the adaptive background modeling using Parzen density estimation, which realizes fast and accurate object detection in input images. We also validate the effectiveness of the proposed system by several experiments.

Original languageEnglish
Pages (from-to)143-152
Number of pages10
JournalProcedia - Social and Behavioral Sciences
Volume2
Issue number1
DOIs
Publication statusPublished - 2010
Event1st International Conference on Security Camera Network, Privacy Protection and Community Safety 2009, SPC2009 - Kiryu, Japan
Duration: Oct 28 2009Oct 30 2009

All Science Journal Classification (ASJC) codes

  • Social Sciences(all)
  • Psychology(all)

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