PREVAIL – Predictive Violence & Aggression Escalation Intelligence Layer

Inspiration

Security teams are often required to monitor multiple surveillance feeds simultaneously, making it difficult to identify and respond to potentially violent situations before they escalate. Most surveillance systems are reactive—they record incidents but provide little intelligence to help operators intervene early.

We wanted to build a system that transforms passive surveillance into proactive safety intelligence. PREVAIL was created to help security operators identify behavioral indicators of aggression, assess escalation risk in real time, and receive actionable alerts before situations become critical.

What it does

PREVAIL is an AI-powered decision support platform that analyzes surveillance feeds and estimates aggression escalation risk in real time.

The platform combines person detection, multi-person tracking, pose analysis, motion analytics, and crowd behavior analysis to generate a live escalation risk score. When elevated risk is detected, PREVAIL provides explainable alerts, contributing factors, and recommended actions to assist human operators.

Key capabilities include:

• Real-time surveillance analysis
• Escalation risk assessment
• Explainable AI insights
• Active security alerts
• Risk timelines and monitoring dashboards
• Human-centered decision support

PREVAIL is designed as a decision-support system and does not perform automated enforcement or decision-making.

How we built it

AI Pipeline

• YOLO11 Pose for person detection and pose estimation
• ByteTrack for multi-person tracking
• Motion and behavioral feature extraction
• Crowd density and convergence analysis
• Risk scoring engine
• Explainability module

Backend

• Python
• FastAPI
• WebSockets
• PyTorch
• OpenCV

Frontend

• Next.js
• React
• TypeScript
• Tailwind CSS

The frontend communicates with the backend through WebSockets, enabling real-time risk updates, telemetry streaming, alerts, and dashboard visualization.

Challenges we ran into

One of the biggest challenges was moving beyond traditional object detection and designing a system capable of understanding behavioral patterns associated with aggression escalation.

Another challenge was integrating multiple AI components into a unified real-time pipeline while maintaining responsiveness and explainability. We also focused on ensuring that operators receive meaningful recommendations instead of raw model outputs.

Building a real-time frontend-backend architecture using WebSockets while keeping the system scalable for future CCTV deployments was another significant challenge.

What we learned

Through PREVAIL, we gained experience in:

• Real-time AI system design
• Computer vision and pose estimation
• Multi-person tracking
• Explainable AI
• WebSocket architectures
• Full-stack application development
• Human-centered safety technology

We also learned the importance of designing AI systems that support human decision-making rather than replacing it.

What's next for PREVAIL

Future development plans include:

• Direct RTSP/IP camera integration
• Multi-camera monitoring
• Large-scale deployment support
• Notification and alert systems
• Cloud-native infrastructure
• Security Operations Center (SOC) integrations

Our long-term vision is to create a scalable human safety intelligence platform that helps organizations proactively identify and respond to potential violence while maintaining transparency, accountability, and human oversight.

Built With

• authentication
• bytetrack
• css
• docker
• fastapi
• jwt
• next.js
• nginx
• opencv
• python
• pytorch
• rbac
• react
• tailwind
• typescript
• websockets
• yolo11