Statistical T+2d Subband Modelling for Crowd Counting

Deepayan Bhowmik, Andrew Wallace

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

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Abstract

Counting people automatically in a crowded scenario is important to assess safety and to determine behaviour in surveillance operations. In this paper we propose a new algorithm using the statistics of the spatio-temporal wavelet subbands. A t+2D lifting based wavelet transform is exploited to generate a motion saliency map which is then used to extract novel parametric statistical texture features. We compare our approach to existing crowd counting approaches and show improvement on standard benchmark sequences, demonstrating the robustness of the extracted features.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
PublisherIEEE
Pages1533-1537
Number of pages5
ISBN (Electronic)9781538646588
DOIs
Publication statusPublished - 13 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Publication series

NameInternational Conference on Acoustics, Speech and Signal Processing
PublisherIEEE
ISSN (Electronic)2379-190X

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2018
CountryCanada
CityCalgary
Period15/04/1820/04/18

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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