AI-Powered Smart Surveillance System
Smart threat detection, facial recognition and real-time alerts for security operations.
Key Details
| Challenge | Security teams needed automated threat detection to reduce manual monitoring and respond faster. |
|---|---|
| Solution | An AI monitoring stack with smart threat detection, facial recognition and real-time alerts integrated with security workflows. |
| Technologies | OpenCV, YOLO, TensorFlow, NVIDIA Jetson, cloud security APIs |
Technologies used
OpenCV
TensorFlow
NVIDIA
Python
Docker
MQTT
Client background
A facilities and security operator monitored dozens of camera feeds manually. Alerts were noisy, night shifts missed events, and compliance reviews required hours of footage scrubbing after incidents.
Key challenges
- Operators could not watch every feed; critical events were easy to miss.
- False alarms trained staff to ignore alerts.
- No on-edge inference — cloud-only processing added latency and bandwidth cost.
- Incident review lacked searchable event timelines.
What we built
- Edge inference on Jetson devices for person, intrusion and anomaly events.
- YOLO-based detection tuned for site-specific lighting and camera angles.
- Alerting console with priority queues and acknowledgement workflows.
- Optional facial recognition modules with privacy controls and audit logs.
Project team: 9 engineers across AI/ML, backend and domain specialists — delivery over 22 weeks.
How we delivered
01
Survey
Walked sites to map camera coverage, lighting and network constraints.
02
Pilot
Deployed on a subset of cameras with human-in-the-loop review.
03
Tune
Reduced false positives with site-specific thresholds and schedules.
04
Scale
Rolled out edge appliances and ops training across facilities.
Business impact
50%Less manual monitoring
40%Faster threat response
HigherCompliance posture