AI Camera Crowd Counting for Dynamic Religious Site Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Traditional crowd management methods in religious sites, such as manual counting and sensor-based techniques, are time-consuming, inaccurate, and impractical in dynamic and unpredictable environments, failing to capture the complex nature of crowd movements, particularly in areas like entrance and exit gates during events with large, unpredictable gatherings.
Innovation Solution
A system utilizing surveillance cameras and AI algorithms, specifically Convolutional Neural Networks (CNNs) to estimate crowd density and send text messages to pilgrims directing them to less crowded areas, thereby managing crowd distribution effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual counting and sensor-based techniques are used for crowd management, then the system is simpler to implement, but the accuracy and efficiency of crowd counting deteriorates
Solution Approach 1:
The patent replaces traditional mechanical crowd counting methods (manual counting and sensor-based techniques) with an AI-based computer vision system using Convolutional Neural Networks (CNNs). The system processes video feeds from surveillance cameras to automatically detect, count, and estimate crowd density, achieving high accuracy without physical sensors or manual intervention.
Solution Approach 2:
The patent uses video feeds from existing surveillance cameras as optical copies of the crowd scenes. Instead of deploying physical sensors throughout the area, the system captures visual information through cameras and processes these image copies using AI algorithms to derive crowd metrics, reducing hardware complexity while maintaining measurement precision.
2Adaptability or versatility
If traditional sensor-based techniques are used for crowd management, then the device complexity is reduced, but the ability to capture dynamic crowd movements deteriorates
Solution Approach 1:
The patent employs a dynamic AI-based system that continuously processes video feeds to track and analyze crowd movements in real-time. The CNN model adapts to varying crowd densities, occlusions, and movement patterns, providing versatile capability to capture dynamic scenarios that static sensor-based systems cannot handle.
Solution Approach 2:
The AI-based crowd management system serves multiple functions: it counts individuals, estimates crowd density, tracks movement patterns, and provides real-time alerts. This multi-functional capability replaces multiple specialized sensors and systems, achieving versatility without proportionally increasing device complexity.
3Productivity
If AI algorithms are deployed for real-time crowd density monitoring, then the productivity of crowd management improves, but the use of energy increases
Solution Approach 1:
The patent pre-trains the Convolutional Neural Network models offline using large datasets of crowd images. This preliminary action allows the models to learn crowd patterns and characteristics beforehand, enabling efficient real-time inference with reduced computational energy requirements during actual deployment.
Solution Approach 2:
The system uses video feeds from existing surveillance cameras as an intermediary data source rather than deploying dedicated high-power sensors. The AI algorithms process these standard video streams efficiently, achieving high productivity in crowd management while minimizing additional energy consumption compared to specialized hardware solutions.
Data Source
AI summary
A method and a system for performing crowd management in a religious site includes receiving registration information from a plurality of pilgrims, including mobile numbers for receiving text messages. The method includes periodically capturing, by a plurality of surveillance cameras, camera view images for views of each camera of respective portions of the religious site. The method includes automatically estimating a number of pilgrims in each camera view image using an Artificial Intelligence (AI) algorithm. The method includes automatically detecting a percentage of mobile numbers of pilgrims occupying overcrowded areas and sending text messages to the detected percentage of mobile numbers that direct the pilgrims to different areas where the camera view images have low crowd density.