The Crowd Control Monitor is a computer vision–based system designed to automatically monitor and manage crowd density in public spaces such as malls, stores, and commercial buildings. Developed in response to the COVID-19 pandemic, the system enforces social distancing guidelines by counting people in real time and generating alerts when occupancy limits are exceeded.
This project demonstrates applied expertise in computer vision, real-time video processing, multithreading, and intelligent surveillance systems, making it well-suited for AI, machine learning, computer vision, and data engineering portfolios.
- Enforce social distancing norms using automated crowd monitoring
- Replace manual and error-prone crowd management methods
- Provide real-time occupancy tracking and alerts
- Leverage existing CCTV infrastructure for cost efficiency
The system processes live CCTV camera feeds using computer vision algorithms to detect and count individuals entering and exiting a monitored area. When the number of people exceeds a predefined threshold, the system automatically triggers alerts to authorities or store managers, enabling immediate corrective action.
Additionally, the system records footfall data, allowing businesses to analyze customer flow patterns and optimize operational decisions.
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Real-Time Crowd Counting
Accurately counts people using live video streams from CCTV cameras. -
Automated Alert System
Generates alerts when maximum occupancy limits are reached. -
Cost-Effective Deployment
Utilizes existing surveillance infrastructure without additional hardware. -
Scalable Architecture
Easily extendable to large areas, multiple stores, or city-wide deployments. -
Optimized Video Processing
Uses threading and performance optimization techniques for real-time processing. -
User-Friendly Controls
Includes schedulers and timers for easy configuration and management. -
Footfall Analytics
Captures and stores entry/exit data for traffic analysis and business insights.
- Programming Language: Python
- Computer Vision: OpenCV
- Machine Learning / Detection Models: Haar Cascades / YOLO / SSD (implementation-dependent)
- Video Processing: Real-time CCTV stream handling
- Concurrency: Multithreading
- Deployment Environment: Local system / Edge devices
- Computer Vision
- Real-Time Video Analytics
- Object Detection and Tracking
- Multithreaded Application Design
- Performance Optimization
- Intelligent Surveillance Systems
- Data Collection and Footfall Analysis
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Video Stream Acquisition
- Capture live video feed from CCTV cameras.
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People Detection & Tracking
- Detect individuals using computer vision models.
- Track entries and exits to maintain real-time count.
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Occupancy Monitoring
- Compare current count against predefined capacity limits.
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Alert Generation
- Trigger alerts when occupancy thresholds are exceeded.
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Data Logging & Analysis
- Store footfall data for trend analysis and reporting.
- Shopping malls and retail stores
- Airports and railway stations
- Corporate offices and campuses
- Hospitals and public institutions
This project addresses a real-world public safety challenge by combining AI-powered surveillance with real-time decision support. It highlights how computer vision can be applied beyond analytics to drive actionable outcomes, contributing to public health, operational efficiency, and data-driven decision-making.
Computer Vision, Crowd Monitoring, People Counting, Real-Time Video Processing,
OpenCV, Object Detection, Multithreading, Footfall Analysis,
AI Surveillance, Public Safety, COVID-19 Solutions