My old vendor NVR had an HDMI output wired to a monitor, on 24/7. You walk into the room and see every camera. Some of those cameras needed replacing, and neither the vendor's own replacements nor other brands would work with the old NVR, so I moved everything to Frigate (Intel N100 mini PC, minimal Debian, Docker). All cameras are in and recording.

What was missing was the screen. Frigate is headless, and its web UI is great for management but not for a wall display. So I wrote XGView: a viewer that pulls the streams and draws a live wall on the HDMI output. On the N100 it runs full screen on minimal Debian + a small window manager, starts at boot, and stays up like the old NVR did. I also run it on a tablet at another location, on a Shield TV, and on a phone.

To be clear, it's not a Frigate replacement. Frigate keeps doing recording, detection and storage. This is only the screen.

What matters for a Frigate setup:

- Frigate import: point it at your Frigate URL, log in, and it reads the API and pairs each camera's main/sub stream by measured resolution. It keeps both the RTSP restream and go2rtc's MSE address. AUTO tries RTSP first and falls back to MSE, so a camera stays watchable when RTSP isn't reachable from where the viewer sits.

- Light on resources: sub streams in the grid, main stream only on the zoomed tile (last sub frame is held until the first main keyframe). Pages you aren't looking at are suspended, sockets and decoders released.

- Hardware decoding via VAAPI on Intel, with an HW/SW badge per tile.

- Layouts: 1x1 to 4x4 plus mixed layouts, camera groups, configurable corner overlays.

- Remote-first: everything works with a TV remote's DPAD + BACK, so the Shield needs no keyboard. Touch-first on tablets and phones, portrait or landscape.

- Also imports from go2rtc, Synology Surveillance Station, ONVIF, or manual RTSP/MJPEG/MSE, if some cameras aren't in Frigate yet.

- Blackout schedule, keep-awake, start on boot with supervised restarts.

Runs on Android TV/phones/tablets (arm64, 8.1+), Windows, Linux and macOS. No account, no cloud, no telemetry. One JSON config file. MIT licensed.

On Linux without desktop installed it needs a display server and a window manager. For a headless-style box, I documented my exact N100 setup: minimal Debian 13 + Xorg + openbox, started by a systemd unit at boot, with Docker for Frigate on the same machine, plus a setup script if you'd rather not do it by hand: https://github.com/xhbl/xgview/blob/master/docs/DEBIAN-KIOSK.md

And very old Android boxes with only OpenGL ES 2.0 are refused rather than half-working.

I'm the author and this is a solo project. Feedback is very welcome, especially on:

- different hardware (mini PCs, Android TV devices, tablets)

- setups where you can only reach Frigate over HTTP (reverse proxy, no RTSP port exposed), since that's where the MSE fallback matters

- anything in the Frigate import that doesn't match your config

Note: This is a personal project, shared as-is, with no warranty. It was developed with AI
assistance under my direction and testing; please review the code before using it in sensitive setups.

Download: https://github.com/xhbl/xgview/releases/latest

(Android APK, Windows, Linux, macOS)

Source (MIT):

https://github.com/xhbl/xgview

Premise:

• Frigate as the NVR engine
• Agent of choice as the glue (I like Hermes)
• Custom video analysis models piped in

Earlier this year, I started messing around with DeepVideo for more complex video analysis:

• https://www.reddit.com/r/frigate_nvr/comments/1s674p8/fun_weekend_project_diy_updated_ai_with_deepvideo/

Since then, two new models have shown up on the scene:

1. JEV (think of this as the next-gen YOLO)
2. Glimpse (think Astra & Opus, but tuned for event-story extraction from video)

JEV is a decision model (seperate from an LLM) & just went multi-modal: (has vision now)

• https://x.com/akshay_pachaar/status/2110997333118972253

Example processing real-time video:

• https://x.com/ErenAILab/status/2111104678943740053

Glimpse is a visual reasoning model:

• https://x.com/deedydas/status/2111342727044665427

This is worth reading:

• https://elorian.ai/blog/introducing-glimpse

What I'm excited about:

• JEV has a crazy fast detection system
• Glimpse can extract stories
• Right now, these are cost-based API's, but the local LLM community is pretty good about conversions these days!

We have really cool stuff like cheap Wyze cameras with official RTSP support, 4K cameras with color nightvision, etc. I'd like to see a 3-way local split:

1. Frigate recording your cameras of choice
2. A JEV-like decision model doing real-time analysis on streams (ex. send to HAOS)
3. A Glimpse-like visual reasoning model collecting a historical story database with conversational access (i.e. easily find stuff & get readable, usable reports)

For example, check out JEV Box Tracking:

• https://x.com/BdcauntBen/status/2110785824723792195

Github:

• https://github.com/BenCaunt/jev-box-tracking/tree/main

Details:

• https://x.com/dave8172/status/2110690268496511133

With agentic video editors, we can also automate a clip story output:

• https://x.com/HyperFrames_/status/2110784695634178256

A few other fun technology peeks:

• 3D digital twinning (ex. HAOS integration)
• Photo to 3D recreation (ex. recreate a car that hit your mailbox)
• Video to Gaussian Splat (recreate a scene capture in 3D...then pop it in your VR headset!)

Very excited to see where this goes!!

Im running Home assistant on a 7500T (I believe) and its kind of a turd for running frigate on it also, I have three unifi cameras on it but they are at higher resolution of the 4k, should I run them lower?

Second any advantages of running Frigate stand alone on another system with docker vs running it with home assistant? I was thinking of upgrading the system at some point and would rather just do it all at once in one system.

Sharing some benchmark comparisons running detection on a 2688×1520 stream directly on an Apple Silicon M1 (8 GB RAM).

Up until recently, my setup ran Frigate 0.18 on Docker (via Colima). To avoid hammering the CPU on a fanless machine, I used the apple-silicon-detector sidecar to offload detection to the M1 GPU. Getting YOLOv9 exported properly took some manual wrangling, but it worked.

I recently tested the same stream on Fregata (the native macOS wrapper/runner for Frigate - https://fregata.app/, https://github.com/3rdBitLabs/Fregata ). Here is how the two implementations compare on identical hardware:

The Metrics Comparison:

Enrichment Metrics on Native (Fregata):

Because CoreML routes separate pipelines cleanly:

• YOLOv9 Plate Detection: ~7.87 ms
• Plate Recognition: ~49.28 ms
• Face Recognition: ~43.97 ms
• Text / Semantic Search Embedding: ~13.61 ms
• Image Embedding: ~76.59 ms

Observations:

1. Onboarding & Model Management: Standard Frigate with apple-silicon-detector required manual model conversions and config adjustments. Fregata pulled down pre-converted CoreML models out of the box on first launch.
2. Resource Separation: The biggest architectural advantage is moving object detection onto the ANE while keeping the GPU available for intermittent enrichment tasks (Face/LPR) and the Media Engine dedicated to decoding/transcoding.
3. Adaptive Transcoding & Search: Natural language search ("find a courier delivery", "person in red shirt") works reliably, and adaptive live streaming is noticeably snappier than running ffmpeg pipelines inside a container engine on macOS.
4. HA Integration: Passes all camera entities, events, and occupancy sensors into my Dockerized Home Assistant setup via my local MQTT broker just like vanilla Frigate.

So I have been running frigate since 3 weeks now, exploring at a slow pace

few items I wanted to check with the community;

• how can i improve the facial recognition?

should I keep assigning detected faces to train the model?
should I use a different model?

what I have done now, is I uploaded few pictures of each face, and assigned few face detected per person,
so far it manages to detect me but not many others
so maybe I have my expectations too high or I need to improve something on my end.

• how to reduce, fix or trouble shoot these false detection

I noticed too many false detection

a cat detected as a person

a baby/chind detected as a cat or as a dog....

how to trouble shoot this?

my config here
https://pastebin.com/YVaFgviR

Hi,

I realize that this is just a minor inconvience, but I noticed when Frigate restarts (due to upgrades, configuration changes, etc.) that the current state of toggles is not maintained. This is, for instance, the case for GenAI review descriptions.

I use GenAI review descriptions only during nights to lower the cost of a paid model, and happily use it to distinguish normal and suspicious behaviour. There is a Home Assistant automation that toggles this GenAI review descriptions switch.

However, in order to be able to use this feature at all, this Frigate feature configuration must be set to 'enabled' but that also sets the default state upon Frigate start and so it enables the feature during daytime. If I disable this feature in the Frigate configuration and want to toggle the review description switch in Home Assistant, it fails with the (Frigate) error:

"GenAI Alerts or Detections must be enabled in the config to be turned on via MQTT."

Does anyone have some advice to implement a workaround before I create a GitHub issue for such a minor inconvience?

Thanks!

I’m trying to get Home Assistant to send notifications to my Android phone from Frigate V18 motion events. I have it working but when I enter a defined Notification Zone of the camera in Frigate, I don’t get the notification from Home Assistant until 45 to 90 seconds after entering the zone.

Notification zone is defined in Frigate.

I’m using the Home Assistant SgtBatten Frigate Notifications Blueprint. If I disable the Blueprint Automation’s "Zone Filter" option, the notification come through immediately.

Any ideas on a work around for this?

I have used blue iris for years. Frigate intrigues me. I have a spare ssd , so I was thinking of setting up frigate on it, so I can easily switch back to blue iris if I dont like it.

My hardware:

I5-8400

No gpu

Good coral

32g ram

256gb ssd

5 hard dives ( 6tb, 4tb,4tb,2tb,1tb)

13 reolink cameras of various generations - oldest i think is called a 310

Am I good to go?

I just deployed Frigate on a docker host VM in my PVE. It runs well on top of around 30 other docker containers, managed by Portainer.
To make a test, I also set it up as privileged LXC on the same PVE. Now I'm wondering what's the best, I've been asking a chatbot about pros and cons of running LXC vs in a VM and honestly in my case I see more benefits of using it along my other docker in the docker host machine.
Ok, managing backup/snapshot would be easier with LXC, but honestly in my case I'm not sure it worth it ?
In term of security, the docker (in VM) will be macvlan isolated, and the VM create kernel boundary with my PVE. Which LXC won't do if privileged.

Can anyone clarify this ?

I run Frigate at home with a handful of Wyze cameras and a doorbell. What I really wanted was a simple way to turn a day of footage into a 1 minute timelapse, without fiddling with the export API or ffmpeg.

That part works now... plus:

- Live grid of all your cameras (WebRTC through go2rtc, with fallbacks)

- Recordings timeline you can zoom from weeks down to seconds, with alerts marked. Click anywhere and every camera plays from that moment, in sync.

- Clips from any stretch of the timeline

- Notifications for new alerts, with a snapshot when it's a person

- A config.yml editor with validation, then save or save & restart

- A Load screen with basic trending of resource consumption.

It's called Chismoso. It's free and open source, runs on Windows, macOS and Linux, and the download is about 10 MB. It talks straight to your Frigate server: no cloud, no account.

Important

- It's a beta (0.3.0). The installers aren't code-signed yet, so Windows and macOS will ask you to confirm the first time.

- It's unofficial and not affiliated with the Frigate project, so please don't file bugs about it there.

- I built a lot of it with an AI coding assistant (Claude). I tested it against my own setup ( windows and Linux ( CentOS and Ubuntu)

I'd love to know if live view works on your setup, and what would make the timelapse part more useful for you.

Edit: forgot to mention, I have only tested and write it against frigate 0.18 and plan to support upcoming versions.

Download: https://chismoso.caprichosos.org

Code: https://github.com/DanielGarc/chismoso

A friend of mine mentioned Frigate when I talked to him about home monitoring for 10 cameras. Why would I use Frigate instead of an NVR software, Blue Iris or other solutions?

I plan on buying a 10-camera NVR kit for home monitoring, when would Frigate make sense?

One of my Amcrest cameras bit the dust and I need a replacement. Any other Brand/Models I should look at?

I know that members of this forum are bound to choose Frigate, but I would it would really help me to understand why you choose Frigate. Thanks

I bought a used HP workstation several years back to run Blue Iris. I'm reasonably happy with it, but the PC is large and loud as hell when the fans get going. I run a half dozen 1080p-ish cameras, all Reolink or Amcrest. All are recording 24/7 to a Synology NAS.

I found a post about a Synology NAS struggling to run it in Docker. I'm not opposed to a replacement miniP PC, depending on price. I don't really care about AI detection, especially if disabling it drops my resource needs ($). All cameras just record 24/7 to my NAS and fall off when the storage is full. I'm happy to do any configuration via my PC through a web interface. Mobile app for checking remotely is a must. Haven't set up Tailscale, but certainly willing to do that if needed.

Is it worth switching? Any former BI users have things to be aware of?

Edit: Thanks all for the helpful replies. I'm going to install Frigate on the Synology and see if I can get it doing what I want as a proof of concept (or the existing BI box if the NAS doesn't have the required horsepower) then buy a dedicated mini PC if things go well.

Dude across the street from me texted me this evening and said his bike was stolen off his front porch last night. We live in a small town, so locking up a crappy old walmart bike isn't exactly a high priority.

The night time footage I had of the thieves across the street wasn't great.

But, Frigate came in clutch. I was able to use the explore feature and pull up all the other recent videos of people on bikes going past. Again, I live in a small town, not a lot of traffic. About half the bikes going past my house are ridden by amish people. I found a very similar looking guy.

Then, I used Claude to compare the clip of the 2 guys that stole the neighbors bike (one of them was on a bike when they went past, the thief was walking) against the daytime video I found of a similar looking person. It was able to determine that the bike was the same size (BMX bike) and possibly color (front bars are gold) and the same build of person.

Obviously not definitive evidence, its not like I'm gonna get NCIS on this case, lol. But now I'm like 95% certain I know who stole it, and if I see them around I'll know to keep an eye on them.

Not sure if this kind of post is OK here, if not I've got no issue with it being taken down.

Hi everyone, I have my frigate set up with 3 different cameras with person detection on all 3 and some masks and zones set up. I've got a coral edge m.2 accelerator on the machine that hosts the frigate instance. I've just gotten it set up not too long ago and have been monitoring it's performance. A few times a minute usually, I get a warning saying one of the cameras is using higher than normal CPU (30%+). This wouldn't be an issue as it usually only lasts for a second or so, but any time this happens the feed that Home Assistant is receiving and displaying cuts out until it returns to under 30%, sort of defeating the purpose of having it tied into HA. I also notice that even though I have masks enabled, I still get a ton of recordings of things inside the mask zones.

Since I'm pretty new to frigate, I was hoping I could post my setup here and statistics and get some advice on how to improve. Overall, my setup is a Beelink s12 with the coral edge m.2 installed (I know it's out of support, I can't update it but it still works until I can find a replacement). Proxmox hosts Home Assistant and Frigate. Frigate has been allocated 3 CPU cores (of 4 total) and 6GB memory (of 16). Both of these stay pretty consistently under 30% usage.

My config is below. I got the settings offline from the forums or YouTube for setup, so I've got no idea if it's actually optimal. I mostly want to avoid things that may cause issues or the spikes in usage without knowing it. Any advice is welcome

mqtt:
  host: IP # <---- same mqtt broker that home assistant uses
  user: username
  password: password

auth:
  session_length: 60

ffmpeg:
  hwaccel_args: preset-vaapi

detectors:
  coral:
    type: edgetpu
    device: pci

model:
  width: 320
  height: 320
  input_tensor: nhwc
  input_pixel_format: rgb
  input_dtype: int
  path: /edgetpu_model.tflite

record:
  enabled: true
  motion:
    days: 7
  alerts:
    retain:
      days: 5
      mode: motion

detect:
  fps: 5
  enabled: true
objects:
  track:
    - person
    - umbrella

snapshots:
  enabled: true
  retain:
    default: 15

cameras:
  reolink_doorbell_front:
    ffmpeg:
      inputs:
        - path: rtsp://username:password@cameraip
          roles:
            - detect
            - record

    motion:
      enabled: true
      mask:
        - 0.024,0.665,0.051,0.82,0.101,0.971,0.252,0.884,0.234,0.737,0.14,0.645
        - 0.329,0.699,0.522,0.713,0.52,0.657,0.456,0.63,0.384,0.626,0.332,0.644
        - 0.71,0.676,0.66,0.634,0.698,0.572,0.752,0.625
        - 0.829,0.644,0.898,0.641,0.902,0.545,0.871,0.526,0.833,0.552
        - 0.998,0.569,0.829,0.553,0.792,0.334,0.72,0.283,0.653,0.052,0.887,0.054,0.925,0.121,0.998,0.102
        - 0.78,0.467,0.322,0.466,0.345,0.402,0.208,0.286,0.061,0.218,0.083,0.152,0.139,0.11,0.258,0.117,0.395,0.149,0.405,0.086,0.654,0.054,0.722,0.281,0.79,0.334,0.809,0.453
        - 0.629,0.543,0.608,0.574,0.714,0.573,0.673,0.542
        - 0.316,0.563,0.58,0.572,0.606,0.577,0.62,0.547,0.567,0.56,0.317,0.553
        - 0.708,0.564,0.995,0.569,0.713,0.577
        - 0.319,0,0.318,0.042,0.66,0.042,0.659,0
    zones:
      frontpath:
        coordinates: 
          0.106,0.974,0.253,0.878,0.363,0.943,0.552,0.702,0.523,0.7,0.523,0.652,0.474,0.634,0.503,0.621,0.641,0.628,0.57,0.996,0.115,0.997
        loitering_time: 0
        friendly_name: FrontPath
        objects:
          - person
          - umbrella
    review:
      alerts: {}
    objects:
      filters:
        person: {}
  amcrest_turret_back:
    ffmpeg:
      inputs:
        - path: rtsp://username:password@cameraip/
          roles:
            - detect
            - record

    motion:
      enabled: true

      mask:
        - -0.001,0.526,0.535,0.346,0.536,0.007,0.001,0.002
        - 0.583,0.341,0.685,0.284,0.99,0.403,0.994,0.268,0.542,0.007,0.54,0.332
    zones:
      deck:
        coordinates: 
          0.001,0.54,0.553,0.339,0.827,0.445,0.844,0.519,0.826,0.588,0.932,0.656,0.994,0.446,0.997,0.997,0.001,0.997
        loitering_time: 0
        objects:
          - person
          - umbrella
        friendly_name: Deck
        inertia: 3
      yard:
        coordinates: 
          0.595,0.346,0.687,0.287,0.988,0.408,0.929,0.647,0.835,0.581,0.853,0.514,0.835,0.438
        loitering_time: 0
        objects:
          - person
          - umbrella
        friendly_name: Yard
  amcrest_turret_garage:
    ffmpeg:
      inputs:
        - path: rtsp://username:password@cameraip/
          roles:
            - detect

    motion:
      mask:
        - 0.211,0.429,0.276,0.413,0.418,0.307,0.437,0.144,0.272,0.001,0,0,-0.001,0.513,0.134,0.426
        - 0.344,0.36,0.468,0.345,0.724,0.314,0.998,0.315,0.998,0.301,0.845,0.291,0.704,0.296,0.687,0.208,0.43,0.214,0.417,0.305,0.383,0.332
        - 0.273,0,0.438,0.144,0.43,0.218,0.844,0.203,0.998,0.234,1,0
        - 0,1,0.054,0.753,0.311,0.743,0.477,0.927,0.66,0.916,0.777,1,0.194,1
    zones:
      driveway:
        coordinates: 0.777,1,0.662,0.916,0.47,0.344,0.728,0.315,0.998,0.528,1,1
        loitering_time: 0
        friendly_name: Driveway
        objects:
          - person
          - umbrella
version: 0.17-0
camera_groups:
  Front_Yard:
    order: 1
    icon: LuHouse
    cameras:
      - reolink_doorbell_front
      - amcrest_turret_garage

The documentation affiliate links seem outdated - none are in stock.

Where should I go next? Home security use, want several cameras outdoor + doorbell. Dedicated NVR options desired as well, to use frigate for detection with.

Are theere certain specs/features to look for? IP, what MP? Etc

Thanks.

I installed a few Dahua cameras and a Reolink doorbell to deal with local vandals trashing the place. It's been mostly effective but there is an outside area I can't see. Not possible to put a fixed camera up as it's not my property on that side (common area) but there a good view through the window. Had a quick look online for suitable unitsbut nothing jumped out at me. It needs to be wifi with USB power. Any suggestions? It needs to have good low light capability but not IR (it's behind glass after all)

I'm not selling these but I got a couple. So far, so good. Thought Frigate crowd may be interested.

Warning: These are HUGE. They are going to look very awkward on a house. Think big like the head of the skibbidy toilet character. You can't hang these from any random soffit area. They are heavy and need significant structural support. Pole mount and JB were included with mine. The pics make it look like a fairly regular bullet but the scale is anything but that.

4MP, 1/1.8", 6mm-145mm, p-iris, 150m smart IR. All the stream settings you'd expect in a high end camera. LPR not built in, but custom exposure can lock in <1ms shutter and <50 gain if you keep WDR off so perfect for Frigate analytics as long as your model has a license plate class.

$75 shipped, UNV, not off brand. One had a flock sticker on it. 10 or so left.

If you’re not reading plates from across the neighborhood, you can probably look into somebody’s soul with this thing. My lowly Dahua 5 series turrets feel junky now.

ebay itm# 298564193061

edit: Party’s over. They’re asking $200 now instead of $69. probably fair but not PSA material.

My zoneminder install took a dump this week... so I started checking out alternatives. Why wasn't I using this earlier?! The object detection is just great right out of the box. I've got 10 cameras on an old i7 ProDesk that I had lying around. I have some issues with the console and h265 encoding on a couple of the cams, but that appears to be a linux browser issue - not the software. But overall.....great stuff. That is all.

Would I be completely wasting my time and money trying to make Frigate work with one or two WiFi cameras? If not can any suggest a specific model that’s known to work well?

Hi All,

I understand heic files aren't currently supported for uploading face images to train face recognition but just wanted to check if there are plans to support heic files in the future. Thanks

I have record setup on my camera and can see recordings, however, the state classification does not seem to be grabbing state images. The object classification engine is working though. Anyone have an idea why I'm not seeing training samples?

  # Reolink FEP
  garage:
    enabled: true
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/garage
          roles:
            - record
            - detect
            - audio
          input_args: preset-rtsp-restream
    detect:
      width: 720
      height: 720
    live:
      streams:
        Stream 1: garage
    audio:
      enabled: true
    zones:
      zone_garage:
        coordinates: 
          0.272,0.088,0.328,0.169,0.474,0.127,0.635,0.139,0.666,0.042,0.979,0.037,0.995,0.99,0,1,0,0.009
        enabled: true
        inertia: 3
        loitering_time: 0
      zone_driveway:
        coordinates: 
          0.292,0.094,0.333,0.165,0.474,0.125,0.63,0.139,0.648,0.078,0.467,0.062
        enabled: true
        inertia: 3
        loitering_time: 0
    review:
      alerts:
        required_zones:
          - zone_driveway

...................

classification:
  bird:
    enabled: false
  custom:
    Cars:
      enabled: true
      name: Cars
      threshold: 0.8
      object_config:
        objects:
          - car
        classification_type: sub_label
    Family Motorcycles:
      enabled: true
      name: Family Motorcycles
      threshold: 0.8
      object_config:
        objects:
          - motorcycle
        classification_type: sub_label
    Car Charging:
      enabled: true
      name: Car Charging
      threshold: 0.8
      state_config:
        cameras:
          garage:
            crop:
              - 0.35403136036397453
              - 0.2264359831245339
              - 0.45779287174264244
              - 0.3301974945032018
        motion: true
        interval: 120
      save_attempts: 100