Statistical t+2D subband modelling for crowd counting

BHOWMIK, Deepayan and WALLACE, Andrew (2018). Statistical t+2D subband modelling for crowd counting. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Calgary, Canada, 15-20 April, 2018.

[img]
Preview
PDF
ICASSP2018-CROWD.pdf - Accepted Version
All rights reserved.

Download (292kB) | Preview
Official URL: https://2018.ieeeicassp.org/Default.asp
Related URLs:

    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 statical 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.

    Item Type: Conference or Workshop Item (Paper)
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Cultural Communication and Computing Research Institute > Communication and Computing Research Centre
    Departments - Does NOT include content added after October 2018: Faculty of Science, Technology and Arts > Department of Computing
    Depositing User: Deepayan Bhowmik
    Date Deposited: 23 Mar 2018 11:10
    Last Modified: 16 Nov 2018 11:58
    URI: http://shura.shu.ac.uk/id/eprint/18596

    Actions (login required)

    View Item View Item

    Downloads

    Downloads per month over past year

    View more statistics