Abnormal crowd density estimation in aerial images

Mliki Hazar; Olfa Arous; Mohamed Hammami

doi:10.1117/1.JEI.28.1.013047

26 February 2019 Abnormal crowd density estimation in aerial images

Mliki Hazar, Olfa Arous, Mohamed Hammami

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 1, 013047 (February 2019). https://doi.org/10.1117/1.JEI.28.1.013047

Abstract

The unpreceded growth of intelligent surveillance systems has resulted in an urgent need for automatic analysis of the captured scenes. Automatic detection of an abnormal crowd in aerial images can provide useful information to prevent disasters. In fact, aerial images have the advantage of covering a very large view of the people distributed over a scene. Accordingly, we propose a high density crowd detection method in aerial images. In this method, we adapted the bag of words technique using the multiblock local binary pattern as a texture descriptor to extract low-level features. In addition, we also used a three-level classification strategy to reduce confusion between crowd density classes. The experimental results reveal the performance of our method while estimating the crowd density in a challenging context.

Citation Download Citation

Mliki Hazar, Olfa Arous, and Mohamed Hammami "Abnormal crowd density estimation in aerial images," Journal of Electronic Imaging 28(1), 013047 (26 February 2019). https://doi.org/10.1117/1.JEI.28.1.013047

Received: 19 July 2018; Accepted: 8 February 2019; Published: 26 February 2019

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