Graduate School and Research Center in Digital Sciences

Crowd density analysis using subspace learning on local binary pattern

Fradi, Hajer; Zhao, Xuran; Dugelay, Jean-Luc

ICME 2013, IEEE International Workshop on Advances in Automated Multimedia Surveillance for Public Safety, July 15-19, 2013, San Jose, CA, USA

Crowd density analysis is a crucial component in visual surveillance for security monitoring. This paper proposes a novel approach for crowd density estimation. The main contribution of this paper is two-fold: First, we propose to estimate crowd density at patch level, where the size of each patch varies in such way to compensate the effects of perspective distortions; second, instead of using raw features to represent each patch sample, we propose to learn a discriminant subspace of the high-dimensional Local Binary Pattern (LBP) raw feature vector where samples of different crowd density are optimally separated. The effectiveness of the proposed algorithm is evaluated on PETS dataset, and the results show that effective dimensionality reduction (DR) techniques significantly enhance the classification accuracy. The performance of the proposed framework is also compared to other frequently used features in crowd density estimation. Our proposed algorithm outperforms the state-of-the-art methods with a significant margin.

Document Doi Bibtex

Title:Crowd density analysis using subspace learning on local binary pattern
Keywords:Crowd density, local binary pattern, dimensionality reduction, classification
City:San Jose
Department:Digital Security
Eurecom ref:4002
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Bibtex: @inproceedings{EURECOM+4002, doi = {}, year = {2013}, title = {{C}rowd density analysis using subspace learning on local binary pattern}, author = {{F}radi, {H}ajer and {Z}hao, {X}uran and {D}ugelay, {J}ean-{L}uc}, booktitle = {{ICME} 2013, {IEEE} {I}nternational {W}orkshop on {A}dvances in {A}utomated {M}ultimedia {S}urveillance for {P}ublic {S}afety, {J}uly 15-19, 2013, {S}an {J}ose, {CA}, {USA}}, address = {{S}an {J}ose, {UNITED} {STATES}}, month = {07}, url = {} }
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