Sorry, you need to enable JavaScript to visit this website.

Statistical t+2D Subband Modelling for Crowd Counting

Citation Author(s):
Deepayan Bhowmik, Andrew Wallace
Submitted by:
Deepayan Bhowmik
Last updated:
13 April 2018 - 3:34am
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Deepayan Bhowmik
Paper Code:
4554

Abstract 

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.

up
0 users have voted:

Dataset Files

ICASSP2018-poster-dbhowmik.pdf

(297)