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Local, on-device AI object detection for privacy and low latency
Open-source NVR with optional paid enhancements via Frigate+
Edge computing architecture—no cloud required
Frigate+ models trained on real security footage for higher accuracy
User fine-tuning with your own annotated images (multiple times per year)
Package and delivery logo recognition
Facial recognition support
License plate recognition (ALPR) support
Generative AI for descriptive, intent-focused alerts
Zone-based object tracking and precise event triggers
Smart streaming that adapts bandwidth and switches to live on activity (jsmpeg, MSE, WebRTC)
Deep Home Assistant integration; MQTT, OpenHab, and Node-RED support
Video recording with configurable retention (default 10 days for tracked objects)
Hardware-accelerated video decoding (e.g., Intel iGPU) to reduce CPU usage
High throughput detection (100+ detections per second with supported accelerators)
Significant false-positive reduction compared to motion-only NVRs
Real-time sensors and low-latency camera entities for automations
Privacy-first design—camera feeds stay on your network
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Get reliable porch and driveway alerts by detecting people, packages, and vehicles while avoiding false motion alarms.
Monitor parking lots and storefronts with accurate vehicle and person detection, plus optional license plate recognition.
Use zones to trigger alerts when someone enters lobbies, stairwells, or restricted areas, and archive relevant clips.
Create Home Assistant automations based on object type (person, car, pet) and precise zone events.
Detect wildlife activity without flooding notifications from wind, rain, or shadows.
Receive package and carrier-logo notifications and trigger automations like porch lights or chimes.
Combine license plate recognition with gate controls and logging for access management.
Keep all camera footage local with no cloud requirement while still benefiting from advanced AI.
Identify after-hours intrusions and filter out routine motion to reduce alert fatigue.
Deploy edge-accelerated detection (e.g., EdgeTPU) across many cameras with efficient hardware utilization.