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TempleGuard AI: Smart People Counting & Crowd Management Platform
TempleGuard AI is an AI-powered crowd management and visitor analytics platform designed for temples and large public gatherings. Using computer vision and real-time people counting, the system monitors CCTV feeds at entry and exit points, tracks visitor movement, provides live occupancy insights, and enables centralized monitoring across multiple temple locations.
Introduction
TempleGuard AI is an AI-powered crowd management and visitor analytics platform designed for temples and large public gatherings.
Using computer vision and real-time people counting, the system monitors CCTV feeds at entry and exit points, tracks visitor movement, provides live occupancy insights, and enables centralized monitoring across multiple temple locations.
Tech Stack
React.js
Python (Django)
PostgreSQLChallenges
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Managing Large Crowd Volumes
Temples experience significant fluctuations in visitor traffic, especially during festivals, religious events, weekends, and peak hours, making crowd monitoring difficult.
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Lack of Real-Time Visibility
Administrators had limited visibility into live crowd density, occupancy levels, visitor movement, and camera activity across different areas and locations.
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Manual Visitor Counting
Traditional visitor counting methods were inaccurate, resource-intensive, and unsuitable for high-footfall environments.
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Distributed Multi-Location Operations
Monitoring crowd activity across multiple temple locations through separate systems made centralized management and reporting challenging.
Our Approach
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AI-Based Real-Time People Counting
Implemented YOLO-powered computer vision models to detect, count, and track visitor movement through entry and exit camera feeds in real time.
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Live Crowd Monitoring Dashboard
Developed a centralized dashboard displaying visitor counts, entry and exit activity, camera health, occupancy levels, and crowd statistics across monitored locations.
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Historical Analytics and Trend Analysis
Built analytics modules to visualize peak hours, visitor trends, crowd density patterns, date-wise comparisons, and temple-level performance metrics.
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Multi-Location Management Platform
Created a centralized management system that enables administrators to monitor multiple temples, manage camera infrastructure, control user access, and generate operational reports from one interface.
Key Features
AI-Powered Real-Time People Counting
Uses YOLO-based computer vision models to detect, count, and track visitors moving through entry and exit points in real time.
Live Crowd Density and Occupancy Monitoring
Provides administrators with live visibility into current occupancy, entry counts, exit counts, and crowd density levels across monitored areas.
Centralized Multi-Temple Monitoring Dashboard
Enables administrators to monitor multiple temple locations, cameras, visitor counts, occupancy levels, and operational alerts from a single platform.
Historical Visitor Analytics and Trend Reporting
Generates interactive analytics for peak hours, daily visitor trends, temple-wise footfall, crowd patterns, and attendance comparisons.
Camera Health and Stream Monitoring
Tracks RTSP camera availability, feed status, connection health, and missing footage conditions to support reliable surveillance operations.
Scalable GPU-Accelerated AI Processing
Uses GPU acceleration, queues, and background processing systems to handle continuous camera feeds and support scalable AI inference across multiple locations.
Results & Impact
TempleGuard AI successfully transformed traditional crowd monitoring into an intelligent, scalable, and data-driven visitor management ecosystem. By combining computer vision, real-time people counting, centralized monitoring, historical analytics, and multi-location management, the platform established a strong foundation for improving public safety, operational efficiency, and crowd control across temples and large public gathering environments.
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