u/Gullible_Low_1742

Demo: https://youtu.be/FNZ72Bq5KGI

Hey all, I'm the developer of Grablo Vision and wanted to share it here. It's free for personal, non-commercial use.

The idea is simple: point AI detection at cameras you already own, then build rules with no code. When something is detected, run these actions.

It scales the way you'd expect: add as many cameras as you want, run multiple AI analyzers on a single camera, and attach multiple rules (automations) to each analyzer.

Detection (4 types)

• Object Detection: pick a class (e.g. person), set a confidence threshold and a region of interest
• Face Recognition: enroll faces, then trigger on known vs. unknown
• License Plate (LPR): read plates and match against a registered list
• Fire Detection

Camera input: RTSP, ONVIF, or a directly connected USB camera.

Triggers & conditions

• Detected / not detected (or known/unknown, registered/unregistered)
• Sustained-for duration, so a brief flicker doesn't set it off
• Schedule: restrict a rule to certain days and hours
• Ask LLM: a plain-language confirmation step before acting, e.g. "only if the person is doing something suspicious or dangerous" (Object detection)

Actions, chained freely in sequence. Stack as many as you want, in any order, with delays between them. For example: announce over a speaker, wait 5s, send a photo, then turn on a light. Available actions:

• Notify: Push, Telegram, Email
• Log the event with a snapshot
• Text-to-Speech announcement, or Play Audio/Video
• Control external devices via Zigbee or I/O relays (open a gate, flip a light or siren)
• MQTT publish or HTTP request (webhook)
• Delay between steps

Home Assistant integration: separate from the rules above, each AI analyzer can expose its detection state directly as a Home Assistant sensor, so your existing HA automations can react to it.

Runs on hardware you already have: a Raspberry Pi, Jetson, or any Linux / Windows / macOS machine (x86 or ARM). It's also available as a Docker image or a Home Assistant add-on.

Detection runs on your own machine and streams peer-to-peer, so footage isn't sitting in someone else's cloud.

A few things people set up with it

• Face: send a photo alert when an unknown face appears, and log known faces silently
• License plate: raise the parking barrier for a registered plate, and keep a log of every plate that pulls in with a timestamp and snapshot
• Fire: sound a siren and send an immediate alert if fire appears in the garage
• Person, at night only: announce "someone is at the door" over a speaker between 10pm and 6am
• Suspicious behavior (LLM check): if a person is loitering or acting oddly, flip a relay to turn on a light and push a notification

It's still evolving, so I'd genuinely welcome feedback and feature ideas.

Link: https://vision.grablo.co

Hey all, I'm the developer of Grablo Vision and wanted to share it here. It's free for personal, non-commercial use.

The idea is simple: point AI detection at cameras you already own, then build rules with no code. When something is detected, run actions.

It scales the way you'd expect: add as many cameras as you want, run multiple AI analyzers on a single camera, and attach multiple rules (automations) to each analyzer.

Detection (4 types)

• Object Detection: pick a class (e.g. person), set a confidence threshold and a region of interest
• Face Recognition: enroll faces, then trigger on known vs. unknown
• License Plate (LPR): read plates and match against a registered list
• Fire Detection

Camera input: RTSP, ONVIF, or a directly connected USB camera.

Triggers & conditions

• Detected / not detected (or known/unknown, registered/unregistered)
• Sustained-for duration, so a brief flicker doesn't set it off
• Schedule: restrict a rule to certain days and hours
• Ask LLM: a plain-language confirmation step before acting, e.g. "only if the person is doing something suspicious or dangerous" (Object detection)

Actions, chained freely in sequence. Stack as many as you want, in any order, with delays between them. For example: announce over a speaker, wait 5s, send a photo, then turn on a light. Available actions:

• Notify: Mobile Push, Telegram, Email
• Log the event with a snapshot
• Text-to-Speech announcement, or Play Audio/Video
• Control external devices via Zigbee or I/O relays (open a gate, flip a light or siren)
• MQTT publish or HTTP request (webhook)
• Delay between steps

Home Assistant integration: separate from the rules above, each AI analyzer can expose its detection state directly as a Home Assistant sensor, so your existing HA automations can react to it.

Runs on hardware you already have: a Raspberry Pi, Jetson, or any Linux / Windows / macOS machine (x86 or ARM). It's also available as a Docker image or a Home Assistant add-on.

Detection runs on your own machine and streams peer-to-peer, so footage isn't sitting in someone else's cloud.

A few things people set up with it

• Face: send a photo alert when an unknown face appears, and log known faces silently
• License plate: raise the parking barrier for a registered plate, and keep a log of every plate that pulls in with a timestamp and snapshot
• Fire: sound a siren and send an immediate alert if fire appears in the garage
• Person, at night only: announce "someone is at the door" over a speaker between 10pm and 6am
• Suspicious behavior (LLM check): if a person is loitering or acting oddly, flip a relay to turn on a light and push a notification

It's still evolving, so I'd genuinely welcome feedback and feature ideas.

Link: https://vision.grablo.co

I've got a few older IP cameras and got tired of the basic motion alerts — every passing car, every shadow. Getting real detection usually means a monthly cloud subscription, or wiring up a whole DIY stack yourself, and I didn't want either.

So I made software that adds on-device AI + automation to cameras you already have — it detects what's on the feed, then acts on it. It's a copy-paste template with a setup wizard, which is the part that makes it genuinely easy to get going. It runs on whatever you've got — a Raspberry Pi for one camera, a mini-PC or GPU box for more.

Demo: https://youtu.be/ql1QFqpsSuc

To be upfront: it's not fully local — the phone push and the app go through a server — but the AI detection and the video stay on your own machine, and you choose what (if anything) gets sent out.

How it works: point it at a camera (USB, RTSP, or ONVIF), pick what to detect, then turn on whatever you want it to do when it sees something:

• Logging — keeps a snapshot + record you can scroll back through
• Push notification — a ping on your phone
• Play media — plays an alarm or sound on a speaker
• Voice (TTS) — speaks a warning out loud
• Home Assistant — shows up as a sensor you can automate off
• MQTT — publishes to a broker
• HTTP — POSTs to a webhook, your NVR, whatever
• Zigbee — flips a Zigbee device (light, siren, plug)
• Relay / GPIO — fires a relay for a siren, light, or gate

They're independent switches, so you can run just logging, or wire it into your whole setup.

If you want to try it: install Grablo, copy the template from the gallery, and the wizard does the rest. It's free for personal and non-commercial use. I'm the dev, so happy to answer anything or take feature requests — three templates (object detection, face recognition, license plates), links in the comments.

Adding face recognition to HA usually means some setup — config files, automations to wire up, and fairly heavy hardware.  This is just one template instead: copy it, and a setup wizard does the rest. It's light enough to run on a Raspberry Pi 4+.

You enroll people from a built-in dashboard (a few photos each), and it recognizes known faces in real time and flags unknown ones.

It all runs on-device — recognition, enrollment, and the logs stay local and never go to the cloud. The only thing that leaves your network is the push notification.

After copying, the wizard walks you through it:

• Camera — USB/CSI, RTSP, or ONVIF (ONVIF is auto-discovered)
• Recognition — Fast vs Accurate mode, optional GPU acceleration, and anti-spoofing
• Features — pick what happens on a match. Logging, push notification, and Home Assistant are three independent switches; turn on any combination

What you get:

• Logging — the name and a snapshot of every recognition, to review later
• Push notification — a ping on your phone the moment a face is recognized
• Home Assistant — each match shows up as a "Recognized Name" sensor entity you can automate off (unlock for known people, alert on unknown, and so on)

Quick start

• Install Grablo — whatever fits: Home Assistant add-on (easiest on HA OS/Supervised), Docker, or standalone on a Pi 4/5, Jetson, Windows, or Mac
• Hit "Copy to my projects" on the gallery template
• Open the project, and the wizard launches automatically
• Enroll a face from the Manage Faces dashboard and recognition starts

There's a phone dashboard for the live feed + recent recognitions too (needs the Grablo app).

It's free. Link's in the comments — happy to answer any questions. And if there's a feature you'd want that isn't there, let me know.

Most ANPR is either a cloud subscription or a pile of setup, so I made this as a copy-paste template instead. It runs on your own device. Copy it, and a setup wizard walks you through four steps:

• Camera: USB/CSI, RTSP, or ONVIF (ONVIF auto-discovered)
• Recognition: Fast or Accurate mode. Latin (EU / US / South America) or Korea plates.
• Filter: min confidence and hold time. A plate must read steadily for 3s before it counts, so misreads drop out.
• Plates and alerts: upload your known-plate list. Get a push on known, unknown, or both. Every read is logged.

What you get:

• A push the moment a plate is read (e.g. only on plates not on your list)
• A timestamped log of every plate, to review entries/exits later
• A phone dashboard with live feed and recent plates (needs the Grablo app)

Quick start

• Install Grablo (Pi 4/5, Jetson Nano, Windows, or Mac)
• Hit "Copy to my projects" on the gallery template
• Open the project, and the wizard launches automatically

It's free, link's in the comments.

Happy to answer any questions.

And if there's a feature you'd want that isn't there, let me know. Trying to figure out what's worth building next.