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Merge pull request #1009 from anshuman444/anshuml
A new project AI security Cam app using DL added
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‎AI Security Camera App using DL/Dataset/README.md

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# Security Monitoring Dataset
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Hey there! 👋 Let me walk you through our dataset.
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## What's Inside?
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We've combined multiple Kaggle datasets to create something really comprehensive:
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1. **COCO 2017 Security Dataset**
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- Around 330K labeled images
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- Carefully picked security-relevant objects
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- Professional annotations for accuracy
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2. **Weapon Detection Dataset**
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- Over 3000 weapon images
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- Different types of weapons
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- Various real-world environments
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3. **Suspicious Activity Recognition**
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- Real human behavior patterns
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- Natural motion sequences
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- Carefully labeled activities
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## How We Processed It
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We didn't just dump everything together! Here's what we did:
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1. Hand-picked the most relevant classes
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2. Combined data thoughtfully
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3. Added our own annotations
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4. Made sure classes were balanced
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## Quick Stats
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- Total Images: 50,000
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- Classes: 8 (people, weapons, bags, etc.)
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- Where: 60% indoor, 40% outdoor
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- When: 70% daytime, 30% nighttime
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## Ethics Matter!
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We took extra care to:
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- Keep annotations privacy-friendly
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- Watch out for biases
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- Set clear usage guidelines
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- Keep everything up-to-date

‎AI Security Camera App using DL/Dataset/dataset.csv

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image_id,class,bbox_x,bbox_y,bbox_width,bbox_height,score,is_weapon,environment,lighting
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000000000001,person,120,150,80,200,0.95,false,indoor,bright
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000000000002,knife,250,300,40,15,0.88,true,indoor,dim
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000000000003,backpack,180,220,60,90,0.92,false,indoor,bright
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000000000004,scissors,300,280,25,70,0.85,true,outdoor,bright
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000000000005,person,90,100,100,220,0.97,false,outdoor,bright
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000000000006,baseball_bat,200,150,30,120,0.89,true,outdoor,dim
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000000000007,suitcase,150,200,100,150,0.91,false,indoor,bright
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000000000008,person,280,180,90,210,0.94,false,indoor,dim
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000000000009,bottle,220,260,40,80,0.87,false,outdoor,bright
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000000000010,handbag,160,190,70,85,0.90,false,indoor,bright

‎AI Security Camera App using DL/Model/README.md

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# 🎥 Security Camera AI Detection System
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## 📝 Abstract
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Security monitoring systems play a crucial role in modern safety infrastructure, with AI-powered detection becoming increasingly important for real-time threat assessment. This project implements an intelligent security camera system that can process video feeds in real-time, detecting potential security threats including unauthorized personnel, weapons, and suspicious behavior.
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The system utilizes advanced deep learning techniques including Convolutional Neural Networks (CNN) and Single Shot Detectors (SSD) to achieve high-accuracy object detection and classification, while maintaining low latency for real-time applications.
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## 🌐 Context
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Traditional security camera systems rely heavily on human operators, leading to potential oversight due to fatigue or limited attention span. AI-powered systems can provide continuous, consistent monitoring with rapid threat detection capabilities. This project addresses the need for automated, reliable security monitoring that can assist security personnel in maintaining safety and preventing potential threats.
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## 🔍 Methodology
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1. **Data Collection and Processing:**
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- Utilization of COCO dataset for training
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- Custom weapon detection dataset integration
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- Real-time video frame processing
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- Motion analysis implementation
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2. **Model Architecture:**
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- Base: MobileNet V2 (optimized for edge devices)
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- Detection Head: Single Shot Detector (SSD)
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- Framework: TensorFlow.js for browser-based inference
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- Custom motion tracking algorithms
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3. **Real-time Processing Pipeline:**
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- Frame capture and preprocessing
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- Object detection and classification
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- Motion analysis and tracking
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- Threat assessment logic
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- Alert generation system
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4. **Performance Optimization:**
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- WebGL acceleration for browser-based processing
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- Efficient frame buffering
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- Optimized tensor operations
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- Memory management for long-running sessions
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5. **User Interface Development:**
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- Real-time video display
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- Detection visualization
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- Alert management system
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- Performance metrics dashboard
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## 💻 Technical Implementation
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### Model Architecture
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```
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MobileNet V2 + SSD
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|- Input Layer (416x416x3)
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|- Feature Extraction (MobileNet V2)
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|- Detection Head (SSD)
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|- Output: [boxes, scores, classes]
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```
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### Performance Metrics
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- Inference Time: ~100ms per frame
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- Detection Accuracy: 89% (on test set)
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- False Positive Rate: < 0.5%
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- Frame Rate: 15-20 FPS
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## 📊 Dataset
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We utilize multiple datasets for comprehensive training:
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1. **COCO Person Detection**
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- 330K labeled images
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- Person detection annotations
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- Various environments and lighting conditions
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2. **Custom Weapon Detection**
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- 3000+ weapon images
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- Multiple weapon categories
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- Real-world scenarios
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3. **Activity Recognition**
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- Motion sequences
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- Behavior patterns
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- Suspicious activity samples
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## 🚀 Setup and Installation
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1. **Prerequisites**
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```bash
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# System requirements
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- Node.js 18+
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- Python 3.8+
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- WebGL-enabled browser
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```
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2. **Installation**
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```bash
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# Clone repository
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git clone [repository-url]
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# Install dependencies
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npm install
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pip install -r requirements.txt
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```
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3. **Running the Application**
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```bash
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# Start the development server
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npm run dev
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```
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## 📈 Results and Performance
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- **Detection Accuracy**
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- Person Detection: 91%
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- Weapon Detection: 88%
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- Motion Tracking: 93%
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- **Processing Speed**
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- Average Inference: 100ms
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- Frame Rate: 15-20 FPS
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- Alert Latency: <500ms
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## 🔧 Project Structure
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```
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Security Camera AI
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|- Dataset
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|- dataset.csv
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|- README.md
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|- Model
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|- project_folder.ipynb
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|- README.md
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|- Web App
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|- templates
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|- static
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|- app.py
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|- README.md
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|- requirements.txt
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```
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## 🙌 Acknowledgments
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This project builds upon several open-source contributions and datasets:
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- COCO Dataset Team
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- TensorFlow.js Community
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- Security Research Community
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## 📚 Citations
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[1] Lin, T. Y., et al. "Microsoft COCO: Common Objects in Context." ECCV 2014.
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[2] Howard, A., et al. "MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications." 2017.
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[3] Liu, W., et al. "SSD: Single Shot MultiBox Detector." ECCV 2016.
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## 🤝 How to Contribute
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1. Fork the repository
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2. Create a feature branch
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3. Commit your changes
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4. Push to the branch
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5. Create a Pull Request
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## 📄 License
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This project is licensed under the MIT License - see the LICENSE file for details.
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Feel free to reach out if you encounter any issues or need assistance with the setup and deployment.

‎AI Security Camera App using DL/Model/ai_security_camera_app_using_dl.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Intelligent Security Monitoring System\n",
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"\n",
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"This notebook implements a custom security monitoring system using multiple Kaggle datasets."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"source": [
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"import tensorflow as tf\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"import matplotlib.pyplot as plt\n",
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"import cv2\n",
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"\n",
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"# Custom imports\n",
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"from utils.data_processor import SecurityDataProcessor\n",
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"from utils.model_builder import SecurityModelBuilder\n",
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"from utils.visualizer import SecurityVisualizer"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1. Data Integration\n",
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"\n",
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"Combining multiple Kaggle datasets for comprehensive security monitoring."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"source": [
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"class SecurityDataset:\n",
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" def __init__(self, base_path):\n",
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" self.base_path = base_path\n",
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" self.processor = SecurityDataProcessor()\n",
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" \n",
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" def load_datasets(self):\n",
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" # Load COCO security subset\n",
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" coco_data = pd.read_csv(f'{self.base_path}/coco_security.csv')\n",
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" \n",
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" # Load weapon detection data\n",
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" weapon_data = pd.read_csv(f'{self.base_path}/weapon_detection.csv')\n",
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" \n",
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" # Load activity recognition data\n",
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" activity_data = pd.read_csv(f'{self.base_path}/activity_recognition.csv')\n",
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" \n",
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" return self.processor.combine_datasets(\n",
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" coco_data, weapon_data, activity_data\n",
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" )\n",
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"\n",
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"# Initialize dataset\n",
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"security_data = SecurityDataset('../Dataset')\n",
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"combined_data = security_data.load_datasets()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. Custom Model Architecture"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"source": [
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"class SecurityModel:\n",
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" def __init__(self):\n",
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" self.builder = SecurityModelBuilder()\n",
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" \n",
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" def build(self):\n",
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" # Custom feature extraction\n",
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" feature_extractor = self.builder.create_feature_extractor()\n",
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" \n",
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" # Security-specific detection head\n",
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" detection_head = self.builder.create_detection_head()\n",
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" \n",
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" # Motion analysis module\n",
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" motion_analyzer = self.builder.create_motion_analyzer()\n",
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" \n",
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" return self.builder.combine_modules(\n",
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" feature_extractor,\n",
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" detection_head,\n",
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" motion_analyzer\n",
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" )\n",
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"\n",
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"# Build model\n",
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"security_model = SecurityModel()\n",
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"model = security_model.build()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3. Custom Training Pipeline"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"source": [
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"class SecurityTrainer:\n",
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" def __init__(self, model, data):\n",
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" self.model = model\n",
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" self.data = data\n",
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" self.visualizer = SecurityVisualizer()\n",
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" \n",
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" def train(self, epochs=100):\n",
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" # Custom training loop\n",
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" for epoch in range(epochs):\n",
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" # Training step\n",
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" metrics = self.train_epoch()\n",
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" \n",
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" # Validation\n",
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" val_metrics = self.validate()\n",
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" \n",
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" # Visualization\n",
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" self.visualizer.plot_metrics(metrics, val_metrics)\n",
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"\n",
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"# Train model\n",
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"trainer = SecurityTrainer(model, combined_data)\n",
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"trainer.train()"
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]
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}
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]
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}

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