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An Efficient Approach to Feature Extraction for Crowd Density Estimation
Published in Springer
Volume: 810
Pages: 345 - 354
Crowd feature extraction is important step for crowd density estimation. This paper proposes a simple and novel approach of feature extraction applicable for crowd density estimation. A 5×5 mask is proposed for extraction of density, which finds isolated components in the image. This helps in classification of the density in five levels using SVM and ANN classifiers. The method can be used for intelligent surveillance system in public places. It can easily be used for embedded applications. © Springer Nature Singapore Pte Ltd. 2019.
About the journal
JournalData powered by TypesetComputing, Communication and Signal Processing
PublisherData powered by TypesetSpringer
Open AccessNo