Transforming Railway Stations for Mahakumbh 2025
450M+
Pilgrims Secured
8+
Video Analytics
Use cases
Use cases
70%
Faster Emergency
Response
Response
3000+
Actionable Event
Intelligence
Intelligence
3000+
Cameras
Introduction
In early 2025, India witnessed one of the world’s largest religious gatherings, drawing hundreds of millions of pilgrims to the city of Prayagraj. This unprecedented influx of travelers placed immense pressure on the region’s transportation infrastructure, particularly its railway stations, which served as the primary entry and exit points for the majority of visitors.To support the safe and efficient movement of passengers, Awiros, in coordination with Railway Authorities, deployed its advanced AI-powered FacialRecognition and Video Analytics System at six major railway stations: Prayagraj Junction, Prayag, Sangam, Phaphamau, Jhunsi, and Rambag. This deployment aimed to enhance station security, improve surveillance accuracy, and streamline crowd management during one of the most logistically demanding times in recent history.
The Challenge
The massive influx of passengers at railway stations around Prayagraj during the peak of the religious gathering presented several critical challenges forrailway authorities, including:
• Unauthorized Access: High foot traffic increased the risk of people entering restricted areas, posing significant security threats.
• Security Incidents: The surge in passengers increased risks of lost individuals, theft, and vandalism, while surveillance systems faced threats of intentional damage or obstruction.
• Manual Monitoring Gaps: Manual surveillance lacked real-time threat detection capabilities, making it challenging for security personnel to track movements effectively.
• Slow Emergency Response: Lack of automated alerts delays identifying and assisting individuals in distress, increasing safety concerns.
• Unauthorized Access: High foot traffic increased the risk of people entering restricted areas, posing significant security threats.
• Security Incidents: The surge in passengers increased risks of lost individuals, theft, and vandalism, while surveillance systems faced threats of intentional damage or obstruction.
• Manual Monitoring Gaps: Manual surveillance lacked real-time threat detection capabilities, making it challenging for security personnel to track movements effectively.
• Slow Emergency Response: Lack of automated alerts delays identifying and assisting individuals in distress, increasing safety concerns.
The Solution
Facial recognition:
Enabled real-time identification of suspects and missing persons by matching faces against a secure database.
Camera Tampering Detection:
AI algorithms monitored for any obstructions, rotations, or physical damage to surveillance cameras, ensuring uninterrupted surveillance.
Attribute-Based Search:
Security teams gained the ability to search for individuals using physical attributes such as clothing color, gender, or other distinguishing features. This feature was particularly useful in locating missing individuals or suspects
Human & Vehicle Tracking:
Real-time monitoring of people and vehicles across station premises improved situational awareness and response.
Fallen Person Detection:
The system automatically identified individuals who had collapsed, enabling faster medical response.
Combination Search:
Multi-parameter search capabilities empowered authorities to swiftly identify and track individuals or vehicles, using a combination of attributes, improving investigation efficiency.
Loitering Detection:
AI-Enabled cameras tracked passenger movement and flagged individuals lingering insensitive areas, helping prevent suspicious activity.
Color-Based Search:
Authorities leveraged AI-powered color filters to locate specific individuals or objects efficiently, improving the speed & accuracy of security checks.
Intrusion Detection:
AI-enabled cameras installed at platform ends and control rooms automatically detect unauthorized access, helping prevent trespassing and accidents.
Implementation Strategy
Awiros collaborated closely with railway authorities and system integrators to ensure a smooth and effective deployment of the surveillance system.Deploying this advanced system required a structured approach to ensure seamless integration with existing security protocols. The key steps undertaken were:
Strategic Collaboration: Awiros worked hand-in-hand with station officials to deploy and calibrate the AI powered system, ensuring it aligned with operational workflows and station layouts.
Integration with Control Rooms: The video analytics platform was seamlessly integrated into existing control room infrastructure, enabling real-time monitoring through AI-enhanced dashboards and alert systems.
Real-Time Alerts for Proactive Monitoring: The system delivered instant alerts for events such as intrusions, tampering, and fallen individuals, allowing security personnel to respond quickly and effectively.
Scalability and Future Expansion: The system was designed with scalability in mind, ensuring adaptability for security needs. This ensures that railway authoritie scan use the technology beyond the event period.
Strategic Collaboration: Awiros worked hand-in-hand with station officials to deploy and calibrate the AI powered system, ensuring it aligned with operational workflows and station layouts.
Integration with Control Rooms: The video analytics platform was seamlessly integrated into existing control room infrastructure, enabling real-time monitoring through AI-enhanced dashboards and alert systems.
Real-Time Alerts for Proactive Monitoring: The system delivered instant alerts for events such as intrusions, tampering, and fallen individuals, allowing security personnel to respond quickly and effectively.
Scalability and Future Expansion: The system was designed with scalability in mind, ensuring adaptability for security needs. This ensures that railway authoritie scan use the technology beyond the event period.
The Impact
The deployment of Awiros’ AI-powered Facial Recognition and Video Analytics System at key railway stations near Prayagraj delivered measurable improvements in safety, efficiency, and incident response. Outcomes included:
1. 70% Reduction in Unauthorized Access: Proactive monitoring and real-time alerts significantly reduced security breaches.
2. 40% Improvement in Passenger Flow: AI-driven tracking helped manage crowd more efficiently, reducing congestion.
3. Incident Response Time Cut Over 70%: Average response time dropped from 10 minutes to under 3minutes, enabling faster interventions.
4. 50% Increase in Threat Detection Efficiency: Enhanced surveillance capabilities led to quicker identification and resolution of security threats.
5. 500+ Missing Persons Reunited: Attribute-based search tools enabled authorities to locate and assist lost individuals swiftly.
6. 30% Drop in Vandalism and Petty Crimes: The presence of intelligent surveillance acted as a deterrent to unlawful activities.
7. 95% Accuracy in Threat Detection: High Detection accuracy minimized false alarms and improved realtime decision making.
1. 70% Reduction in Unauthorized Access: Proactive monitoring and real-time alerts significantly reduced security breaches.
2. 40% Improvement in Passenger Flow: AI-driven tracking helped manage crowd more efficiently, reducing congestion.
3. Incident Response Time Cut Over 70%: Average response time dropped from 10 minutes to under 3minutes, enabling faster interventions.
4. 50% Increase in Threat Detection Efficiency: Enhanced surveillance capabilities led to quicker identification and resolution of security threats.
5. 500+ Missing Persons Reunited: Attribute-based search tools enabled authorities to locate and assist lost individuals swiftly.
6. 30% Drop in Vandalism and Petty Crimes: The presence of intelligent surveillance acted as a deterrent to unlawful activities.
7. 95% Accuracy in Threat Detection: High Detection accuracy minimized false alarms and improved realtime decision making.
Conclusion
The deployment of Awiros’ AI-powered surveillance system at key railway stations during the largest religiousgathering set a new benchmark for safety, security, and crowd management at large-scale public events. By leveragingreal-time video analytics, facial recognition, and AI-driven insights, railway authorities successfully ensured a safe,secure, and efficient travel experience for millions of pilgrims.This implementation not only demonstrated the immediate benefits of AI in public safety but also provided a scalableand replicable model for future large-scale events. The success of this project underscores the potential of AI-poweredsecurity solutions in transforming surveillance and incident management strategies worldwide.
Smarter insights. Better outcomes. Start with Awiros.
