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AI Detection & Smart Alerts

AI that runs on
your hardware, not theirs.

YOLOv8 object detection runs entirely on your GPU. 80 object classes, smart anomaly-based alerting, and per-camera filters — no API keys, no monthly AI fees, no cloud dependency.

Try AI Detection Free How Alerts Work
Detection Performance
Real numbers from a real home lab install.
These are live stats from a 34-camera deployment on an RTX 3070. Your results will vary by hardware and scene complexity.
Avg Inference Time
14ms
RTX 3070, YOLOv8n
Detections Today
847
Person + Vehicle + Animal
False Alert Rate
<2%
With motion zone filters
Cameras on AI
34
Full YOLO on all streams
Detection Classes
What LookoutNVR can see.
YOLOv8 recognizes 80 object classes from the COCO dataset. The three categories that matter most for security are front and center — but everything else is available for filtering and alert rules.
Person
Detects individuals in frame. Separate from motion detection — triggers only when a person is actually present, not shadows or swaying trees.
Vehicle
Cars, trucks, motorcycles, bicycles, buses, and trains. Know when your driveway or parking area has a new arrival without checking footage.
Animal
Cats, dogs, birds, horses, cows, sheep, bears, deer, and more. Filter them out of security alerts or create dedicated wildlife monitoring rules.
Full Object Catalog
All 80 detectable object classes.
Every class can be used in per-camera filters and alert rules. Whitelist only what you care about, or blacklist what you don't.
People
person
Vehicles
car truck motorcycle bicycle bus train airplane boat
Animals
bird cat dog horse sheep cow elephant bear zebra giraffe
Outdoor & Street
traffic light fire hydrant stop sign parking meter bench
Accessories & Bags
backpack umbrella handbag suitcase tie
Sports & Recreation
frisbee skis snowboard sports ball kite baseball bat baseball glove skateboard surfboard tennis racket
Kitchen & Food
bottle wine glass cup fork knife spoon bowl banana apple sandwich orange broccoli carrot hot dog pizza donut cake
Indoor & Furniture
chair couch potted plant bed dining table toilet sink refrigerator microwave oven toaster
Electronics & Personal
tv laptop mouse remote keyboard cell phone book clock vase scissors teddy bear hair drier toothbrush

Model: YOLOv8n (nano) via ONNX Runtime. 640×640 input resolution. Default confidence threshold: 50%.

Per-Camera Filters
Every camera sees different things.
Filter accordingly.
Each camera has independent class filters. Whitelist only the classes you care about, blacklist the ones that cause noise, or use a preset to get started in one click.
Security Preset
People & vehicles only
Whitelist: person, car, truck, motorcycle, bicycle, bus. Everything else is ignored. Best for driveways, entrances, and parking areas.
person car truck motorcycle bicycle bus
Farm Preset
Ignore common animals
Blacklist: bird, cat, dog, cow, horse, sheep, bear, zebra, giraffe, elephant. Alert on people and vehicles. Best for barn and field cameras.
bird cat dog cow horse sheep
Wildlife Preset
Animals only
Blacklist: person, car, truck, motorcycle, bicycle, bus. Alert on animal activity only. Best for trail cams and nature monitoring.
person car truck cat dog bear
Custom filters
Don't want a preset? Set any combination of allowed and ignored classes per camera. The AI Suggest feature analyzes your last 72 hours of detections and recommends which classes to filter based on what's actually showing up on each camera.
GPU Compatibility
Works with what you already have.
NVIDIA, AMD, and Intel Arc all supported. CPU fallback for headless setups.
GPUAPIMax Cameras (AI)Inference SpeedNotes
NVIDIA RTX 3080 / 4070+CUDA60+8–12msBest performance
NVIDIA RTX 3070 / 4060CUDA30–4012–18msRecommended tier
NVIDIA GTX 1080 / RTX 2060CUDA16–2420–35msGood for home setups
AMD RX 7700 / 6700+ROCm / DirectML16–3015–28msWindows + Linux
Intel Arc A770OpenVINO12–2025–40msWindows only currently
CPU Fallback (i7/i9)ONNX Runtime4–880–150msWorks, slower
Smart Motion Zones
Draw zones. Eliminate noise.

AI detection alone eliminates 80% of false alerts. Combine it with inclusion and exclusion zones to get that number below 2%. Draw the driveway as an inclusion zone. Draw the road as an exclusion zone. Done.

  • Per-camera zone drawing in the UI
  • Inclusion and exclusion zones
  • Combine with AI class filters (person only, vehicle only)
  • Time-of-day scheduling per zone
DRIVEWAY — INCLUSION
ROAD — EXCLUDE
GarageFront · 02/28/26 14:32
Smart Alerting
Alerts that learn what's normal.
LookoutNVR doesn't just detect objects — it classifies how unusual each detection is based on 7 days of history per camera. A person at the front door at 3 AM is treated differently than at 3 PM.
How the detection pipeline works
1
Motion Pre-Filter
Before running AI inference, each frame is checked for motion. If nothing has changed (below the per-camera motion threshold), the frame is skipped entirely. This saves GPU cycles on static scenes.
2
YOLOv8 Inference
Frames with motion are run through the YOLO model on your GPU. Objects are detected with bounding boxes and confidence scores. Only detections above the confidence threshold (default 50%) pass through.
3
Zone & Class Filtering
Detections are filtered against your inclusion/exclusion zones and per-camera class filters. A person detected outside your inclusion zone is discarded. A bird on a camera with the Farm preset is ignored.
4
Anomaly Classification
Each surviving detection is classified as Routine, Unusual, or Rare based on 7-day per-camera history. This determines how aggressively duplicate events are suppressed.
5
Alert Rule Matching
Detection events are checked against your alert rules. Rules can target specific cameras or be global, filter by object class and minimum confidence, and have independent cooldown timers.
6
Notification Dispatch
When a rule matches, notifications are sent via your configured channels — ntfy.sh push notifications, email, webhook, and/or desktop app toasts. All channels fire in parallel.
Anomaly Classification
Not all detections are equal.
LookoutNVR maintains a rolling 7-day frequency baseline for every object class on every camera. Detections are automatically classified into three anomaly levels that control event suppression and alert urgency.
Routine
≥20% of detections in last 7 days
Common activity for this camera. Events are suppressed for at least 5 minutes if the object hasn't moved. Your front door camera seeing a car every 10 minutes won't fill your event log.
Cooldown: 5+ minutes
Unusual
5–20% of detections in last 7 days
Uncommon for this camera. Events are logged with the standard cooldown. A dog at your garage camera that only shows up a few times a day.
Cooldown: 60 seconds (configurable)
Rare
<5% of detections or never seen
Very uncommon or first-time detection for this camera. No suppression — every detection is logged immediately. A person at a back-fence camera at 2 AM.
No cooldown — always passes through
Spatial awareness
Cooldowns only suppress stationary objects. If an object moves more than 10% of the frame width or height, the cooldown resets and a new event is created. A parked car is suppressed after the first event — a car driving through creates events as it moves.
Notification Channels
Get alerted your way.
Configure one or all channels. They fire in parallel — no delay, no single point of failure.
ntfy.sh Push
Push notifications to your phone via ntfy.sh — a free, open-source service. Subscribe to your topic in the ntfy app (iOS & Android) and get instant alerts with priority levels.
Works with self-hosted ntfy servers too.
Email (SMTP)
Send alert emails via any SMTP provider — Gmail, Outlook, Mailgun, your own server. Includes detection details, camera name, confidence score, and timestamp.
TLS on port 587. Configure host, credentials, and recipients.
Webhook
HTTP POST with JSON payload to any URL. Connect to Home Assistant, Node-RED, Slack, Discord, PagerDuty, or any automation you want.
{ event_type, camera, subject, message, timestamp }
Desktop App Toast
Native Windows toast notifications via the LookoutNVR desktop app. Click a toast to bring the app to the foreground and jump to the camera. Works via SignalR — real-time, no polling.
Also shows camera-offline and camera-online status changes.
Alert Rules
Fine-grained control over
what triggers a notification.
Alert rules sit on top of the detection pipeline. A detection event can exist in the timeline without triggering an alert — rules let you decide which detections are worth a push notification at 2 AM and which can wait until you check the event log in the morning.
Rule Setting What It Does
Camera Target a specific camera, or leave blank for a global rule that applies to all cameras.
Object Classes Which detection classes trigger this rule. e.g., only person and car. Leave empty to match all classes.
Min Confidence Minimum confidence score (0–100%) to trigger. Higher = fewer false positives, lower = more sensitive.
Cooldown Minimum seconds between triggers for this rule. Prevents notification storms. Default: 30 seconds.
Notification Channel Where to send: in-app, email, webhook, or push. Different rules can use different channels.
Enabled Toggle rules on/off without deleting them. Useful for temporary rules during construction, events, etc.
Example setup
Rule 1: "Night Watch" — All cameras, person only, confidence ≥70%, ntfy push, cooldown 60s, active 10PM–6AM.
Rule 2: "Driveway Arrivals" — Driveway camera, car + truck, confidence ≥50%, webhook to Home Assistant, cooldown 5 min.
Rule 3: "Package Watch" — Front door camera, all classes, confidence ≥60%, email notification, cooldown 30s, enabled only during delivery hours.
Notification Content
What you get when an alert fires.
NTFY.SH PUSH NOTIFICATION
person detected on FrontDoor
Rule 'Night Watch' triggered. person detected on 'FrontDoor' with 92% confidence.
Priority: high Tags: eyes 03/25/26 02:14:33
WEBHOOK JSON PAYLOAD
{
  "event_type": "detection_alert",
  "camera": "FrontDoor",
  "subject": "person detected on FrontDoor",
  "message": "Rule 'Night Watch' triggered.
    person detected with 92% confidence.",
  "timestamp": "2026-03-25T02:14:33Z"
}
Camera-offline and camera-online events also send notifications — so you know immediately if a camera drops, not when you happen to check the live view.
Built-In Testing
Test before you trust.
The settings page has a "Send Test Notification" button that fires a test alert through all configured channels. You'll see exactly which channels worked and which need attention — before you rely on them at 2 AM.
False Positive Reduction
Six layers between a pixel change
and your phone buzzing.
Most NVR software gives you motion detection and hopes for the best. LookoutNVR stacks multiple independent filtering stages so that by the time an alert reaches you, it's almost certainly real.
1
Motion Gate
AI inference is expensive. If nothing in the frame has changed, the GPU doesn't run. Static scenes are skipped entirely — no wasted compute, no phantom detections from sensor noise.
2
Confidence Threshold
Low-confidence detections are discarded before they enter the pipeline. The threshold is tunable — raise it on cameras with complex scenes, lower it on clean backgrounds where you need maximum sensitivity.
3
Duplicate Suppression
When the model detects the same object multiple times in overlapping bounding boxes, only the highest-confidence detection survives. One person = one detection, not three stacked on top of each other.
4
Spatial Zones
Draw inclusion and exclusion zones directly on each camera's view. A car on the public road doesn't trigger — but the same car in your driveway does. Per-zone class filtering narrows it further.
5
Class Filtering
Per-camera whitelist and blacklist of object classes. Your barn camera can ignore birds and cattle. Your front door camera can only care about people. Different cameras, different rules.
6
Anomaly-Aware Cooldowns
A stationary car in a parking lot doesn't generate an event every 5 seconds. The system tracks object positions and suppresses repeated detections of the same object in the same spot — but resets instantly when something moves or something rare appears.
The result
On a 34-camera system running 24/7, the false alert rate is under 2% with zones configured. Without zones, AI classification alone eliminates about 80% of the noise that pure motion detection produces. Most users configure zones in the first 10 minutes and rarely touch settings again.
Coming Soon
AI is just getting started.
Everything above runs locally on your hardware today. We're working on what comes next — cloud-connected features that build on your local detection engine without replacing it.
License Plate Recognition
Read plates from driveway and parking cameras. Allowlist known vehicles, alert on unknowns.
Audio Anomaly Detection
Detect glass breaking, alarms, shouting, and gunshots from camera audio streams. Per-camera baselines learn what's normal.
Cloud-Assisted Rules
Auto-generate alert rules and filter presets based on detection patterns across your cameras. Your data stays local — the cloud just helps tune.

Pro and Business subscribers get early access to new AI features as they ship.

Your cameras are working.
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