Statistical T+2d Subband Modelling for Crowd Counting

Deepayan Bhowmik; Andrew Wallace

doi:10.1109/ICASSP.2018.8462345

Statistical T+2d Subband Modelling for Crowd Counting

Deepayan Bhowmik, Andrew Wallace

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Publisher	IEEE
Pages	1533-1537
Number of pages	5
ISBN (Electronic)	9781538646588
DOIs	https://doi.org/10.1109/ICASSP.2018.8462345
Publication status	Published - 13 Sept 2018
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing - Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018

Publication series

Name	International Conference on Acoustics, Speech and Signal Processing
Publisher	IEEE
ISSN (Electronic)	2379-190X

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated title	ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	15/04/18 → 20/04/18

ASJC Scopus subject areas

Software
Signal Processing
Electrical and Electronic Engineering

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10.1109/ICASSP.2018.8462345

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Cite this

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title = "Statistical T+2d Subband Modelling for Crowd Counting",

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.",

author = "Deepayan Bhowmik and Andrew Wallace",

year = "2018",

month = sep,

day = "13",

doi = "10.1109/ICASSP.2018.8462345",

language = "English",

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pages = "1533--1537",

booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)",

address = "United States",

note = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",

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Bhowmik, D & Wallace, A 2018, Statistical T+2d Subband Modelling for Crowd Counting. in 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP). International Conference on Acoustics, Speech and Signal Processing, IEEE, pp. 1533-1537, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, Calgary, Canada, 15/04/18. https://doi.org/10.1109/ICASSP.2018.8462345

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AB - 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.

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M3 - Conference contribution

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Statistical T+2d Subband Modelling for Crowd Counting

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