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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.

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Paper Details

Authors:
Deepayan Bhowmik, Andrew Wallace
Submitted On:
13 April 2018 - 3:34am
Short Link:
Type:
Poster
Event:
Presenter's Name:
Deepayan Bhowmik
Paper Code:
4554
Document Year:
2018
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Document Files

ICASSP2018-poster-dbhowmik.pdf

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[1] Deepayan Bhowmik, Andrew Wallace, "Statistical t+2D Subband Modelling for Crowd Counting", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2635. Accessed: Aug. 10, 2020.
@article{2635-18,
url = {http://sigport.org/2635},
author = {Deepayan Bhowmik; Andrew Wallace },
publisher = {IEEE SigPort},
title = {Statistical t+2D Subband Modelling for Crowd Counting},
year = {2018} }
TY - EJOUR
T1 - Statistical t+2D Subband Modelling for Crowd Counting
AU - Deepayan Bhowmik; Andrew Wallace
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2635
ER -
Deepayan Bhowmik, Andrew Wallace. (2018). Statistical t+2D Subband Modelling for Crowd Counting. IEEE SigPort. http://sigport.org/2635
Deepayan Bhowmik, Andrew Wallace, 2018. Statistical t+2D Subband Modelling for Crowd Counting. Available at: http://sigport.org/2635.
Deepayan Bhowmik, Andrew Wallace. (2018). "Statistical t+2D Subband Modelling for Crowd Counting." Web.
1. Deepayan Bhowmik, Andrew Wallace. Statistical t+2D Subband Modelling for Crowd Counting [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2635