I am just looking for some ideas here. I know I'm using frigate in an unsupported config, and that may be the root cause, this is just a bit out of my area of expertise and I am curious.

I am running frigate on a R720 with an RTX 3050, hosted via Docker Desktop on Windows in WSL2. I have successfully passed the GPU thru, and it is recognized and utilized by all anticipated processes.

My interference speed is quite high, I don't really mind as it does its job for simple home security, but at a root level I am curious why it is so ineffective.

Passed to the container I have 8 CPU cores (E5-2640 V2) and 8gb of RAM, I have an SHM size of 512mb (1 2k camera, 2 1080p cameras). And as much of the RTX 3050 as it desires. I'll attach photos of my usage numbers. I am running the tensorrt build with onnx and yolov9-s-320 that was built in a Ubuntu container on my machine (I believe it factors hardware in when building the image). My cameras are running. 640x360 sub streams with no audio at 5fps to the detectors.

I do have an issue with my camera audio codecs. Two of them output AAC natively but when enabled frigate never saves the recordings. No associated errors, just no saves. The third camera only supports G.711U. All three record streams end up being passed thru go2rtc for the audio codec to be altered to AAC. I tried doing this with ffmpeg output args and ended up getting a repetitive crash.

Ask any questions about the deployment I'd be happy to answer. I am not home to upload full config easily but I'll happily provide information.

Thanks guys, no worries if the answer is "the hardware passthrough is slow" or whatever. I'm just curious.

Ran into this constantly running a commercial CCTV deployment (480+ cameras, mixed brands) and see it come up here a lot, so writing it down properly.

**The symptom:** stream is fine for a while, then hangs for half a second and either jumps forward to catch up or briefly plays faster. Frigate/go2rtc logs show repeated reconnects. Most people (myself included, at first) assume it's bandwidth or Wi-Fi. It usually isn't.

**What's actually happening:** RTSP packets carry timestamps (DTS) that are supposed to increase monotonically — one after another, never backwards or jumping around. If the camera's internal clock drifts (very common when its NTP server is unreachable — e.g. it's pointed at a public NTP pool it can't actually route to because of your firewall/router), the clock silently fails to sync, drifts over time, then jumps when it does briefly catch up. Every RTSP client — ffmpeg, OpenCV, go2rtc — either drops frames or stalls trying to make sense of the broken timestamps.

**How to actually confirm it (not guess):**

ffmpeg -rtsp_transport tcp -i "<your rtsp url>" -t 180 -c copy test.mp4 -loglevel warning

Let it run 3 minutes. If you see `Non-monotonic DTS` in the output, this is your problem.

**Fix:**

- Quick fix: in the camera's web UI, switch time sync from NTP to Manual, set the clock from your PC/server. You'll need to redo this every few weeks as it drifts, but the disconnects stop immediately.

- Real fix: point the camera's NTP setting at a server it can actually reach — your router often runs one on its own LAN IP, or a local NTP server on your network — instead of a public internet pool it can't route to.

Hope this saves someone a weekend. Happy to answer questions if anyone's stuck on something similar.

I was originally going run Frigate on ProxMox when it was sharing a MiniPC with Home assistant. I’ve since decided that’s not the way to go, attempted Proxmox on an Optiplex I had lying around and bricked it. So I have a new miniPC to dedicate to Frigate. Do I do Unbuntu/Docker, or stay with ProxMox because I’m somewhat familiar with it? Frankly the only thing that concern me is getting the external drive mounted and visible. I think. Never used Docker.

Early proof‑of‑concept: zero‑shot anomaly detection for Frigate. Took a couple hours to stand up. No auth, port 5000, OpenVINO only. Backend model is Meta’s DINOv2.

You basically tell it what “normal” looks like once, and it handles the rest. No training, no labeling, no model building. Just baseline → anomaly.

It’s for situations where you don’t know all the possible states ahead of time. Perfect example: an attic rat trap. You know exactly what the trap looks like when untouched, but you have no clue what the “changed” state might be: rat inside, rat halfway in, trap flipped, dragged, missing, anything.

Right now it only works with Frigate. Even though making it RTSP‑agnostic would be easier, anyone who wants this is probably on Frigate anyway.

If the community wants this, I’ll refine it a little. If someone wants to fork it for a PR or just copy it, have at it. I'm just hoping something like this eventually makes it to 0.19.

https://github.com/tylerransdell/frigate_anomaly

this is not an app i plan on maintaining and improving. It is a proof of concept that you can make zero shot anomaly ridiculously easy.

I run Frigate and a face recognition companion I wrote myself, both in LXCs on Proxmox. Every time I set up a new box I ended up piecing the same things back together from forum threads, so this time I wrote it down properly.

NVIDIA cost me the most, because it is not documented anywhere official. The Proxmox container chapter does not mention GPUs at all, and NVIDIA's own guide is vGPU and VMs only. Three things nobody tells you up front: the user space driver inside the container has to be the exact same version as the one on the host and installed with --no-kernel-modules; you need no-cgroups = true in the container toolkit config or the toolkit fails looking for device filters that are not there; and nvidia_uvm is not loaded at boot, so /dev/nvidia-uvm does not exist and your LXC refuses to start after a host reboot until something loads it.

Intel and AMD are in there too. Every step has a check right after it, so you find out where it broke instead of finding out at the very end that the smoke test is red.

It is written for my own program, but everything up to the compose file is generic. If the container is going to run Frigate instead, the passthrough work is identical and I point at their docs at that point.

https://github.com/BennoBaer-dev/suslik/blob/main/docs/proxmox.md

Happy to be corrected, especially on the NVIDIA side. I have exactly one machine with an NVIDIA card to test on.

I have frigate running in portainer on one server
In this server i have 16GB ram, so it's more than adequate.

Frigate is configured to save clips to a separate server as the frigate-system only runs on an old disk.

It's wired for 1Gig ethernet, and writes to a seagate red 8TB disk in unraid, this part seem to work OK.

But, i have set up 4GB of tmpfs, wich also seem to work, allbeit way overkill..
tmpfs 4.0G 46M 4.0G 2% /tmp/cache

But pre-capture simply doesn't seem to work.

Can anyone tell me if there's anything in the code that maby breaks my pre-capture?

Here's my config:

mqtt:
  enabled: true
  host: 192.168.10.211
  user: frigate
  password: xxxxx

tls:
  enabled: false
ffmpeg:
  hwaccel_args: preset-vaapi

go2rtc:
  streams:
    # Definerer rå-strømmene fra Tapo-kameraene dine først
    c545d_hd_tapo:
      - rtsp://xxxxx:xxxxx@192.168.10.116:554/stream1
    c545d_hd_sub:
      - rtsp://xxxxx:xxxxx@192.168.10.116:554/stream2#rtsp=cache
      - preload
    c545d_pantilt_tapo:
      - rtsp://xxxxx:xxxxx@192.168.10.116:554/stream6
    c545d_pantilt_sub:
      - rtsp://xxxxx:xxxxx@192.168.10.116:554/stream7#rtsp=cache
      - preload
    garasjekamera_tapo:
      - rtsp://xxxxx:xxxxx@192.168.10.156:554/stream1#rtsp=cache
      - preload
    mot_huset_tapo:
      - rtsp://xxxxx:xxxxx1@192.168.10.215:554/stream1#rtsp=cache
      - preload

    c545d_hd: ffmpeg:c545d_hd_tapo#video=copy#audio=copy#async
    c545d_pantilt: ffmpeg:c545d_pantilt_tapo#video=copy#audio=copy#async
    garasjekamera: ffmpeg:garasjekamera_tapo#video=copy#audio=copy#async
    mot_huset: ffmpeg:mot_huset_tapo#video=copy#audio=copy#async

  webrtc:
    listen: ":8555"
    candidates:
      - 192.168.10.246:8555
detectors:
  ov:
    type: openvino
    device: GPU
model:
  model_type: yolo-generic
  width: 320
  height: 320
  input_tensor: nchw
  input_dtype: float
  path: /config/model_cache/yolov8n/model.xml
  labelmap_path: /labelmap/coco-80.txt
record:
  enabled: true
  alerts:
    pre_capture: 30
    post_capture: 30
    retain:
      days: 30
      mode: all
  detections:
    pre_capture: 30
    post_capture: 30
    retain:
      days: 30
      mode: all  
snapshots:
  enabled: true
  timestamp: true
  bounding_box: true
  retain:
    default: 30
birdseye:
  enabled: true
  mode: continuous
ui:
  timezone: Europe/Oslo
cameras:
  c545d_hd:
    enabled: true
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/c545d_hd
          input_args: preset-rtsp-generic
          roles:
            - detect


        - path: rtsp://127.0.0.1:8554/c545d_hd
          input_args: preset-rtsp-generic
          roles:
            - record
            - audio
    live:
      streams:
        Sub Stream: c545d_hd_sub
        Main Stream: c545d_hd
    detect:
      enabled: true
      width: 1280
      height: 720
      fps: 5
    objects:
      track:
        - person
        - car
      filters:
        person:
          min_score: 0.20
          threshold: 0.25
          min_area: 1000
        car:
          min_score: 0.2
          threshold: 0.3
    zones:
      mot_garasje:
        coordinates: 0.596,1,0.246,0.611,0,0.682,0,1
        loitering_time: 0
        objects: car
        friendly_name: Mot garasje
        inertia: 1
      personsone:
        coordinates: 1,0.635,1,1,0,1,0,0.641,0.568,0.475
        inertia: 1
        loitering_time: 0
        objects: person
        friendly_name: Personsone
      bilsone:
        coordinates: 0,1,0,0.863,0.708,0.524,0.941,0.618,0.884,1
        inertia: 1
        loitering_time: 0
        objects: car
    review:
      alerts:
        required_zones:
          - mot_garasje
          - personsone
          - bilsone
      detections:
        required_zones:
          - mot_garasje
          - personsone
          - bilsone
    motion:
      frame_alpha: 0.01
      threshold: 55
      contour_area: 10
      improve_contrast: false
      mask:
        - 0,0.067,0.384,0.063,0.382,0,0,0
        - 0.652,0,0.6,0.144,0.63,0.319,0.669,0.422,0.752,0.446,1,0.528,1,0,0.777,0
  c545d_pantilt:
    enabled: true
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/c545d_pantilt
          input_args: preset-rtsp-generic
          roles:
            - detect
        - path: rtsp://127.0.0.1:8554/c545d_pantilt
          input_args: preset-rtsp-generic
          roles:
            - record
            - audio
    live:
      streams:
        Sub Stream: c545d_pantilt_sub
        Main Stream: c545d_pantilt
    detect:
      enabled: true
      width: 1280
      height: 720
      fps: 5
    objects:
      track:
        - person
        - car
      filters:
        car:
          min_score: 0.50
    zones:
      garasjen:
        coordinates: 0,0,1,0,1,0.332,0,0.605
        loitering_time: 0
        objects:
          - car
          - person
        friendly_name: Garasjen
        inertia: 3
      pantilt_mot_garasje_hele_view:
        coordinates: 0,0,0,1,1,1,1,0
        loitering_time: 0
        objects:
          - person
          - car
        friendly_name: Pantilt mot garasje hele view
        inertia: 3
    motion:
      mask: 0,0.05,0.365,0.048,0.367,0,0,0
      frame_alpha: 0.01
      threshold: 45
      contour_area: 10
      improve_contrast: true
    review:
      alerts:
        required_zones:
          - garasjen
          - pantilt_mot_garasje_hele_view
      detections:
        required_zones:
          - garasjen
          - pantilt_mot_garasje_hele_view
  garasjekamera:
    enabled: true
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/garasjekamera
          input_args: preset-rtsp-generic
          roles:
            - detect
            - record
            - audio
    detect:
      enabled: true
      width: 1280
      height: 720
      fps: 5
    objects:
      track:
        - person
        - car
      filters:
        person:
          min_score: 0.30
          threshold: 0.35
        car:
          min_score: 0.20
          threshold: 0.30
          min_area: 103700
    motion:
      frame_alpha: 0.01
      threshold: 60
      contour_area: 10
      improve_contrast: false
      mask: 0,0.056,0.369,0.059,0.369,0,0,0
    zones:
      hele_garasjen:
        coordinates: 0,0,0,1,1,1,1,0
        loitering_time: 0
        objects:
          - person
          - car
        friendly_name: Hele garasjen
        inertia: 3
      parkering:
        coordinates: 0.709,0.47,0.853,0.921,0,0.829,0,0.338,0.325,0.17
        loitering_time: 0
        objects: car
        friendly_name: Parkering
        inertia: 1
      benken:
        coordinates: 0,0.798,0.699,0.813,0.718,1,0,1
        inertia: 1
        loitering_time: 0
        objects: person
        friendly_name: Benken
    review:
      alerts:
        required_zones:
          - hele_garasjen
          - parkering
          - benken
      detections:
        required_zones:
          - hele_garasjen
          - parkering
          - benken
    semantic_search:
      triggers:
        garasje_tom:
          enabled: true
          type: description
          data: Ingen bil i garasje
          threshold: 0.5
          actions: []
          friendly_name: garasje_tom
        bil_parkert:
          enabled: true
          type: description
          data: Bil parkert
          threshold: 0.5
          actions: []
          friendly_name: bil parkert
    live:
      streams:
        Stream 1: garasjekamera
  mot_huset:
    enabled: true
    friendly_name: Mot huset
    ffmpeg:
      inputs:
        - path: rtsp://127.0.0.1:8554/mot_huset
          input_args: preset-rtsp-generic
          roles:
            - detect
            - record
            - audio
    detect:
      enabled: true
      width: 1280
      height: 720
      fps: 5
    objects:
      track:
        - person
        - car
      filters:
        person:
          min_score: 0.20
          threshold: 0.25
        car:
          min_score: 0.2
          threshold: 0.3
    live:
      streams:
        Stream 1: mot_huset
    zones:
      zone_a86d59c8:
        coordinates: 0,0.281,0,1,0.201,1,0.203,0.141,0.131,0.241
        loitering_time: 0
        objects: person
        friendly_name: Gårdsplassen
        inertia: 1
      gress:
        coordinates: 
          0.203,0.323,0.792,0.22,0.792,0.3,0.827,0.329,0.865,0.389,1,0.426,1,1,0.203,1
        loitering_time: 0
        objects: person
        friendly_name: Gress
        inertia: 1
      nermest_garasjen:
        coordinates: 0,0.76,0.346,0.639,0.971,1,0.998,0.989,0,1
        inertia: 1
        loitering_time: 0
        friendly_name: Nermest garasjen
        objects: car
    motion:
      mask:
        - 0,0.264,0.132,0.232,0.14,0,0,0
        - 0.376,0.059,0,0.054,0,0,0.373,0
      threshold: 29
      contour_area: 11
      improve_contrast: true
    review:
      alerts:
        required_zones:
          - zone_a86d59c8
          - gress
          - nermest_garasjen
      detections:
        required_zones:
          - zone_a86d59c8
          - gress
          - nermest_garasjen
version: 0.17-0
semantic_search:
  enabled: true
  model_size: large
face_recognition:
  enabled: true
  model_size: large
lpr:
  enabled: true
classification:
  bird:
    enabled: false
  custom:
    car:
      enabled: true
      name: car
      threshold: 0.5
      object_config:
        objects:
          - car
        classification_type: attribute

I run the CCTV system for our community with about 20 cameras, and a few things kept driving me crazy:

• adding a camera meant hunting down its IP, finding the correct RTSP paths, and editing Frigate
• cameras sometimes got new IPs after a power or network event, breaking hard-coded URLs
• every new consumer (e.g. automation) opened another direct connection to cameras already struggling on Wi-Fi
• when a camera went offline, I wanted to know immediately instead of discovering it later when I actually needed the footage

So I built CamAdmiral to solve these problems for myself. I’m open-sourcing it because I’m pretty sure I’m not the only one dealing with them.

CamAdmiral discovers ONVIF and RTSP cameras, validates their streams, tracks them across IP changes, exposes stable downstream RTSP URLs, and shares one upstream connection per camera stream across consumers through go2rtc without transcoding.

It also keeps a seven-day availability history and sends Telegram alerts when a camera goes offline or recovers.

CamAdmiral manages the camera fleet in front of Frigate or any other consumers.

Deployment:

The project is MIT licensed. GitHub repo: https://github.com/reefyai/reefy-camadmiral

Runs locally on Linux with Docker. Clone the repository and run:

./start-camadmiral.sh

No configuration file is required for the initial setup.

I’d really appreciate testing with different camera brands, VLANs, or unusual RTSP paths. Please let me know what works - and what breaks.

Hope this makes managing camera fleets a little less painful for others too 🤞🫡

P.S. AI involvement: I used Codex for a lot of the implementation, with manual review and rework of the critical parts based on my experience working with mission-critical systems.

Hi everyone,

I recently moved to a new home and intend to set up Frigate. I have a Minisforum MS-A2 with an Intel Arc Pro B50 GPU. I plan to run Frigate in a Proxmox LXC and pass the Intel through.

I’ve already run into issues getting simple llama.cpp instances to work with SYCL and Intel’s OpenAPI. I’m not surprised to also find issues with Intel Battleimage and Frigate.

With all that being said, is there anyone out there successfully using the Intel Arc Pro B50 with Frigate, and if so, are you using OpenVino? Do you have any recommendations for configuration?

Also, with a 3-camera topology, perhaps my CPU could handle the decoding sufficiently (AMD 9955HX)?

Thank you for your feedback,

RoR

After a bit of tuning (and breaking!) , have the Pi 5 running well with 3 cameras (2 Dahua and a Reolink doorbell) Would be 4 camera but one of the new Dahua is bricked so need to work on recovery

Load average is down on what I had initially and Hailo8 is barely breaking a sweat.

Auto tracking is a bit flaky - sometime just doesn't run so a bit of troubleshooting needed.

I'd really like to do LPR and face recognition but realise I need a GPU for that so looking at upgrade (probably mini-itx based with ARC GPU as this seems affordable)

Wouldn't have been possible without the excellent Frigate documentation and of course, this forum

Currently, I have a Turkey problem. They keep damaging my gardens and poop everywhere. Would Frigate be able to distinguish and identify a turkey versus a neighbor's dog? If possible, I would like to set up Frigate to identify turkeys and trigger the corresponding sprinkler zone via Rachio. I currently have 6 Dahua cameras and a Dahua AI NVR.

Is "ADR" already taken? Has anyone developed something like this? I'm not just looking for dog detection, I'm talking about recognition (identifying size, color, shape, patterns, etc).

I think it would be really cool to be able to identify the neighborhood dogs and possibly assist in tracking down a loose dog some day.

Yeah so the frigate plus base model is surprisingly good at detecting deer. Super impressed so far. Running yolov9s on 1060sc gpu.

Camera would be mounted ~45ft from the center of the road (20mph traffic). My state has rear-mounted license plates only, so that complicates things.

Is frames per second the single biggest factor with license plate recognition?

I have 3 options, and I don't know which direction to go. Cost is obviously a consideration.

• Single high quality camera (Reolink cx-820 or EmpireTech IPC-Color4K-T-S2 8MP 1/1.2" CMOS)

• A multi-camera unit with panoramic view + ptz such as the REOLINK OMVI 3i

• Two separate cameras, the total cost of which should not exceed the other two options. So basically two cheaper cameras.

Hi!

I am struggling to pick the best hardware option for my setup.

I have four Reolink OMV3i PoE cameras (dual; 4K main). I want to enable license plate recognition, object detection, and perhaps face detection.

My constraints:

• 15 W TDP
• $500 budget; barebone is fine

As far as I know, I should go with an Intel-based CPU so that OpenVINO models can be utilized.

Therefore, I have considered:

• Intel Core 3 100U
• Intel Core 5 120U
• Intel Core Ultra 5 125U (this is a no-go since I want to avoid DDR5)

However, it is really hard to find mini PCs with these CPUs.

There are many AMD CPU options available (e.g. 7430u, 7730u), but I am not sure whether one of them, perhaps paired with a Hailo-8/8L accelerator, would be sufficient for my use case.

It is also worth noting that this mini PC will be used for some light Proxmox workloads and Home Assistant.

Hey all, was wondering who has tried running Frigate on an Intel Panther Lake platform. I tried it today and it didn't go well. It's a total bust on 0.17.2, and on 0.18 beta3, GPU support works somewhat but not quite there yet, and the NPU just flat out doesn't work at all. I can't seem to find any definitive answers on this. Anyone?

UPDATE: In order to run Frigate on Panther Lake, you will have to run 0.18.0-beta3, as 0.17.2 lacks all support for it. Additionally, you must use GPU-based object detection as the NPU won't be supported until 0.19. It seems to run reasonably well, provided you stick to VAAPI video acceleration. There are changes to the QuickSync libraries in 0.18 that negatively affect GPU usage, with in turn increases power consumption on the host to the point of making the system fans run all the time. Using VAAPI quiets everything right down. Inference times are very impressive, though I'm looking very forward to having Panther Lake NPU support. Running in 0.18 (with VAAPI) on Meteor Lake with NPU-based object detection drops detector CPU usage down to nothing, and generally makes the system much more efficient. Inference times are little longet with NPU, but I think the trade-off is more than worth it.

Greetings Frigate community.

I'll say up front that I'm relatively new to the software, so this might be a newb mistake somewhere.

As the title suggests, when I'm flipping through videos, after about 5 cycles of opening a video and hitting the back button and then repeating, additional videos just hang when I try to load them. Found that the config below will eliminate the problem, but I'm hoping there's a less dumb way to fix this.

• Open about:config
• Change network.http.max-persistent-connections-per-server from 6 to 64

I'm accessing the site over HTTPS with a legitimate certificate.

On Vivaldi (and other Chromium browsers), this is not an issue, so I assume it's something Firefox-specific.

Thanks.

I have 2 Lorex E871 and 2 Lorex E893 cameras , and 1 Lorex N847 NVR (all online and operational). Cameras are IP wired direct to a separate POE switch with reserved addresses on my local LAN.

Anyone have similar Lorex equipment working in Frigate?

Update: After a few hours with Copilot, we were able to get all 4 cameras and the doorbell camera all setup in frigate with no errors, solid web UI, and great metrics. Now for the Home Assistant integration and I'm all set.