Density Vision: Crowd Classification using Deep Learning

Estimating crowd density and counting people are essential for crowd control, urban planning, and public safety. This research study utilizes a Multi-Column Convolutional Neural Network (MC-CNN) as a crowd counting technique trained on crowd datasets. The MC-CNN predicts crowd density maps from inpu...

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Bibliographic Details
Published in2024 3rd International Conference on Applied Artificial Intelligence and Computing (ICAAIC) pp. 986 - 992
Main Authors Singh, Harsh Vardhan, Vidhya, S., Kumar, G Santosh
Format Conference Proceeding
LanguageEnglish
Published IEEE 05.06.2024
Subjects
Artificial Intelligence
Decision Tree Classifier
Deep learning
Estimation
Heat Map
K-Means
Multi Column Convolution Neural Network
Neural networks
Object detection
Prediction algorithms
Public security
Refining
Urban planning
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DOI10.1109/ICAAIC60222.2024.10575835

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Summary:Estimating crowd density and counting people are essential for crowd control, urban planning, and public safety. This research study utilizes a Multi-Column Convolutional Neural Network (MC-CNN) as a crowd counting technique trained on crowd datasets. The MC-CNN predicts crowd density maps from input images, providing precise crowd density estimates. Additionally, post-processing methods like clustering analyze the spatial crowd distribution. The proposed system incorporates object detection algorithms to generate individual coordinates, aiding in crowd analysis through clustering and then classifying them into three categories i.e. sparse, medium, and dense. Experimental findings showcase the method's efficacy in accurately estimating crowd densities and discerning crowd patterns. The proposed system provides valuable insights for crowd monitoring, resource allocation, and decision-making in densely populated areas.
DOI:10.1109/ICAAIC60222.2024.10575835