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.
Rol: Machine-learning and web-application engineering
El sistema
Qué se construyó realmente
- 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
Por qué fue difícil
Complejidad de ingeniería
- Keeping frame-by-frame inference responsive on commodity hardware
- Reducing false positives to a level operators can trust
Experiencia de producto
Del sistema entregado
Tecnología
Stack verificado
Solo se listan las tecnologías confirmadas en el código fuente, la documentación del proyecto o el despliegue en vivo.
- Python
- Django
- OpenCV
- Ultralytics YOLO
- Email notification integration
Entrega
Alcance de la responsabilidad
- Model training and evaluation
- Detection pipeline and alerts
- Web application
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