Crowd Density Estimation Using Taylor Expansion and Local Texture Feature

Crowd density estimation from images or videos is an important subject for crowd monitoring and safety control. In this paper, we propose a crowd density estimation method based on the Taylor expansion and the local binary count operator. Crowd density classification is performed using a support vec...

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Bibliographic Details
Published in2019 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-TW) pp. 1 - 2
Main Authors Lai, Chih-Chin, Chiu, Hsien-Chun
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2019
Subjects
Consumer electronics
Estimation
Feature extraction
Safety
Support vector machines
Surveillance
Taylor series
Videos
Visualization
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Summary:Crowd density estimation from images or videos is an important subject for crowd monitoring and safety control. In this paper, we propose a crowd density estimation method based on the Taylor expansion and the local binary count operator. Crowd density classification is performed using a support vector machine. Experiments on the PETS 2009 dataset are provided to demonstrate the feasibility of the proposed approach.
DOI:10.1109/ICCE-TW46550.2019.8992038