Real-time video anomaly detection with automated evidence alerts
Final recorded accuracy of 95.1% before delivery on the trained detection scope.
A Django web application that analyses webcam and uploaded video frame by frame with trained YOLO models, captures anomalies with timestamps and sends automated email alerts, with remote web access.
An applied computer-vision product for monitoring video sources and flagging anomalous events for human review. Detection scope is limited to the classes the model was trained on; this is anomaly detection, not a certified law-enforcement system.
Role: Machine-learning and web-application engineering
The system
What was actually built
- Webcam and uploaded-video input paths
- Frame-by-frame analysis with trained Ultralytics YOLO models
- Anomaly capture with detection timestamps
- Automated email notification with captured evidence
- Remote web access to review detections
Why it was hard
Engineering complexity
- Keeping frame-by-frame inference responsive on commodity hardware
- Reducing false positives to a level operators can trust
Product experience
From the delivered system
Technology
Verified stack
Only technologies confirmed in source code, project documentation or the live deployment are listed.
- Python
- Django
- OpenCV
- Ultralytics YOLO
- Email notification integration
Delivery
Scope of responsibility
- Model training and evaluation
- Detection pipeline and alerts
- Web application
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