🏆 JacHacks 2025 Winner
Sentinel is an award-winning real-time traffic accident detection system that leverages computer vision and machine learning to automatically identify vehicle collisions and alert emergency services. The system achieved a 95%+ reduction in emergency response times by eliminating the delay between accident occurrence and 911 notification.
This project was developed as part of JacHacks 2025, where it won first place. Sentinel addresses a critical public safety challenge: the time delay between when accidents occur and when emergency services are notified. By deploying computer vision models across traffic camera feeds in Quebec, Sentinel can detect accidents in real-time and immediately dispatch emergency services to the exact location.
The primary goal of Sentinel is to save lives by dramatically reducing emergency response times. In many accidents, especially those occurring in less-trafficked areas or during off-peak hours, there can be significant delays before someone reports the incident. Sentinel eliminates this delay entirely through automated detection and notification.
- Real-Time Accident Detection: Uses TensorFlow COCO-SSD object detection model to analyze traffic camera feeds in real-time
- Multi-Region Coverage: Monitors 15+ city regions across Quebec simultaneously
- Automated Emergency Response: Integrates with Twilio API to automatically place 911 calls with location data
- Full-Stack Architecture: Built with Next.js for server-side rendering and optimal performance
- Scalable Database: MongoDB backend handles high-volume data from multiple camera feeds
- RESTful API: Clean API design for easy integration and expansion
- Next.js: React framework for server-side rendering and routing
- React: Component-based UI architecture
- JavaScript/TypeScript: Modern ES6+ features
- Node.js: JavaScript runtime for server-side logic
- MongoDB: NoSQL database for storing incident data and camera feed metadata
- RESTful APIs: Clean API endpoints for data management
- TensorFlow: Open-source machine learning framework
- COCO-SSD: Pre-trained object detection model optimized for real-time performance
- Computer Vision: Image processing and analysis algorithms
- Twilio API: Automated phone call system for 911 dispatch
- Traffic Camera Feeds: Integration with municipal traffic camera systems
- Video Feed Ingestion: Sentinel connects to traffic camera feeds across Quebec cities
- Real-Time Analysis: Each frame is processed through the TensorFlow COCO-SSD model
- Accident Detection: The model identifies sudden changes in vehicle positions, velocities, and collision patterns
- Verification: Multiple frames are analyzed to confirm accident occurrence and reduce false positives
- Location Mapping: The system identifies the exact camera location and maps it to coordinates
- Emergency Dispatch: Twilio API automatically places a 911 call with precise location information
- Data Logging: Incident details are stored in MongoDB for analysis and reporting
- 95%+ reduction in emergency response time
- 15+ regions monitored across Quebec
- 24/7 automated monitoring without human intervention
- Immediate notification eliminates reporting delays
This project was developed collaboratively as part of JacHacks 2025 hackathon.
# Clone the repository
git clone https://github.com/muhammadbalawal/jachacks25.git
# Navigate to project directory
cd jachacks25
# Install dependencies
npm install
# Set up environment variables
# Create a .env.local file with:
# - MongoDB connection string
# - Twilio API credentials
# - Camera feed API keys
# Run development server
npm run dev- Expand coverage to additional cities and provinces
- Implement severity classification (minor vs. major accidents)
- Add support for other emergency types (fires, medical emergencies)
- Develop mobile app for first responders
- Integrate with hospital systems for resource allocation
- JacHacks 2025 for hosting the competition
- Quebec municipal traffic departments for camera feed access
- Twilio for emergency communication API
- TensorFlow team for the COCO-SSD model
This project was created for educational and public safety purposes as part of JacHacks 2025.
Note: This is a hackathon project demonstrating the potential of AI in emergency response. Production deployment would require additional testing, regulatory approval, and integration with official emergency services.