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.

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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: 18 Mar 2021 08:17

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