Crowd Detection Using Deep Learning (CNN)

Project Overview

This project focuses on detecting crowd density using Convolutional Neural Networks (CNN) and deep learning techniques. The model is trained on crowd images and enhanced using data augmentation and overfitting reduction techniques to improve performance and generalization.

This project was developed as part of a research study and has been presented at a technical conference.

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Objectives

• Detect crowd presence from input images
• Improve model accuracy using data augmentation
• Reduce overfitting using regularization techniques
• Evaluate model performance using confusion matrix and accuracy metrics
• Support research in crowd monitoring and safety systems

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Features

• Image preprocessing and augmentation
• CNN-based deep learning model
• Overfitting reduction techniques
• Model training and validation
• Performance visualization
• Research-based implementation

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📂 Project Structure

Crowd-Detection-Project/
│
├── notebooks/
│   ├── 01_Data_Augmentation.ipynb
│   ├── 02_Model_Training.ipynb
│   ├── 03_Overfitting_Reduction.ipynb
│   ├── 04_Model_Evaluation.ipynb
│
├── paper/
│   └── Crowd_Detection_Research_Paper.pdf
│─ Conference Presentatio/
│   └── PaperPresentationNICEDT-2026.pptx
├── requirements.txt

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📄 Research Paper

This project is based on a research paper presented at a technical conference.

Title: Real-Time Crowd Classification Using Deep Convolutional Neural Networks with Data Augmentation for Enhanced Public Safety

Conference: NICEDT

Year: 2026

Status: Presented (Not yet published online)

The full paper is available here:

📄 paper/Crowd_Detection_Research_Paper.pdf

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⚙️ Installation

Clone the repository:

git clone https://github.com/yourusername/Crowd-Detection-Project.git
cd Crowd-Detection-Project

Install dependencies:

pip install -r requirements.txt

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📊 Model Outputs

The project generates:

• Model Accuracy
• Loss Graphs
• Confusion Matrix
• Performance Evaluation Metrics

Technologies Used

• Python
• TensorFlow / Keras
• NumPy
• OpenCV
• Matplotlib
• Scikit-learn
• Jupyter Notebook

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Dataset

The dataset used in this project is not fully included due to size limitations.

You can:

• Use your own crowd dataset
• Or download from a public crowd dataset source