AI-Powered Real-Time Surveillance Analytics Platform

Overview

AI-Powered Real-Time Surveillance Analytics Platform is a computer vision system that detects, tracks, and analyzes objects from live video streams and recorded footage. Built using the COCO dataset, the platform provides real-time surveillance insights through object detection, tracking, counting, and analytics.

This project demonstrates practical applications of Computer Vision, Deep Learning, Backend Development, and AI System Design.

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Key Features

Real-Time Object Detection

• Detect objects from live camera feeds and videos
• Support for multiple COCO classes
• Confidence-based filtering

Multi-Object Tracking

• Assign unique IDs to detected objects
• Track movement across frames
• Maintain object history

Object Counting

• Count people and vehicles
• Entry and exit monitoring
• Custom counting lines

Zone Monitoring

• Define restricted areas
• Detect unauthorized intrusion
• Real-time alerts

Analytics Dashboard

• Live statistics
• Object distribution analysis
• Historical reports
• Traffic insights

Multiple Video Sources

• Webcam
• CCTV cameras
• RTSP streams
• Uploaded videos

REST API

• Detection API
• Analytics API
• System monitoring endpoints

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System Architecture

Video Source
      │
      ▼
Frame Processing
      │
      ▼
Object Detection
      │
      ▼
Object Tracking
      │
      ▼
Analytics Engine
      │
      ▼
Dashboard & API

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Technology Stack

AI & Computer Vision

• Python
• OpenCV
• YOLO
• NumPy

Backend

• FastAPI
• Uvicorn

Frontend

• React
• Tailwind CSS

Database

• SQLite
• PostgreSQL

Deployment

• Docker
• GitHub Actions

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Supported COCO Classes

People

• Person

Vehicles

• Car
• Bus
• Truck
• Motorcycle
• Bicycle

Everyday Objects

• Backpack
• Handbag
• Bottle
• Chair
• Laptop

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Project Structure

surveillance-platform/
│
├── backend/
│   ├── api/
│   ├── detection/
│   ├── tracking/
│   ├── analytics/
│   └── database/
│
├── frontend/
│   ├── dashboard/
│   └── components/
│
├── models/
├── datasets/
├── videos/
├── tests/
├── docs/
│
├── requirements.txt
├── Dockerfile
└── README.md

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Use Cases

Smart City Monitoring

Monitor traffic flow and pedestrian movement.

Security Surveillance

Detect intrusion in restricted areas.

Parking Management

Track vehicle occupancy and utilization.

Campus Monitoring

Analyze movement patterns across campuses.

Industrial Safety

Monitor operational zones and restricted regions.

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Core Modules

Detection Engine

Performs real-time object detection using deep learning models.

Tracking Engine

Maintains object identities across video frames.

Counting Engine

Counts objects crossing virtual boundaries.

Intrusion Detection Engine

Detects entry into restricted zones.

Analytics Engine

Generates statistics and insights from collected data.

Dashboard Engine

Visualizes real-time and historical analytics.

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Performance Goals

Metric	Target
FPS	30+
Detection Accuracy	>85%
Tracking Accuracy	>90%
API Response Time	<100 ms

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Roadmap

Phase 1

• Video processing pipeline
• Object detection

Phase 2

• Multi-object tracking
• Object counting

Phase 3

• Zone monitoring
• Intrusion detection

Phase 4

• Analytics dashboard
• Reporting system

Phase 5

• Deployment and optimization

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Future Improvements

• License plate recognition
• Crowd density estimation
• Anomaly detection
• Edge-device deployment
• Multi-camera management
• Automated incident reporting

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Installation

git clone https://github.com/your-username/surveillance-platform.git

cd surveillance-platform

pip install -r requirements.txt

python app.py

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Skills Demonstrated

• Computer Vision
• Deep Learning
• Object Detection
• Multi-Object Tracking
• API Development
• System Design
• Data Analytics
• Software Engineering

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License

MIT License

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Author

Dilip Singh

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Built to demonstrate practical AI-powered video analytics using the COCO dataset.