I have 6-8 cameras, frigate runs in VM inside old enterprise gear (R730).
without proper decoder it pulls extra 70w for trans-coding detection and still struggles to keep up.

with coral TPU it goes down to normal levels, but transcoding still consumes likes 8 cores, not sure why? Maybe to feed data into TPU? I added GPU with pass through (which is limited on R730), everything started to run smoothly. But then my server just restarts out of blue like every second day.

This is proxmox -> VM -> docker(frigate). it is not easy to kill host machine. I am out of ideas, it is very nice, but it needs dedicated somehow modern machine to run.

Can someone recommend off-shelf NVR that could do somehow decent job, and then could passtrough streams to frigate when I am ready to fight it again? Most of the (better ones) cameras I have are reolink.

I love how it works, I love the project, but currently it consumes too much power to be justified with EU electricity prices 😞

I have Frigate 0.17 running in a docker container under Portainer connecting to mqtt and sending events.

The issue is that it only works for me if I set the password directly in the Frigate configuration file.

I'd like to pass the password via an environment variable, so I can keep the configuration file in git.

I tried various variations of

password: "{FRIGARE_MQTT_PASS}"

(with or without quotes, single or double quotes, with or without $)

but nothing works. It just doesn't connect to mqtt and Home Assistant shows no events, except when I set the literal password in Frigate's config (then HA shows events)

What am I missing?

Thanks.

the docs seem to have older links? one is dead, two cameras are less than the best size sensor, another link im unfamiliar with brand.

i want high quality 4K for inside my garage when im bringing clients cars in. so like, my cell phone takes better video than most of these cameras im finding. i want as high quality picture as possible for a couple of my cameras so i can pause and zoom in on frames.

then i want a couple good for oudoors, i want to read plates and see details from about 120ft away. ptz would be nice to work on auto for wide coverage. poe of course and it gets to -35f here.

i have a couple reolinks in the house but i NEVER got them working right. seems they are less than supported on frigate? the streams were always glitchy - good quality but every 4 or so frames id loose a frame. futsed with it for months to no avail.

so what are the best/most supported cameras these days?

I bought some Dahua cameras. I couldn't get rtsps working and I did the mistake of updating one of them so now there's a big fat Dahua logo on the stream and as far as I can tell there's no configuration for removing it.

I bought some Reolink cameras. There simply isn't an option for rtsps and they come with a few other minor annoyances.

This made me wonder where are the simple open cameras which doesn't have a huge closed ecosystem? Basically the Frigate friendly cameras? By open I mean some camera producer which allows the end user to modify, or flush, their own firmware.

How do I create an ONVIF username/password for my G6 PTZ so I can use it in Frigate? I can't find ONVIF settings anywhere in Protect.

I set up my home surveillance almost 10 years ago. Dahua NVR came highly recommended to me. When I first hooked it up, i was kinda shocked at how terrible it was. I never could get motion events to work and the remote access was flaky. Whenever id get a new phone, thered be a new app thatd take me hours to get the remote access working.But, it recorded and I could view the cameras, so, I lived with it.

Over the weekend, I started putting in a homelab server. I have about 40 dockers running with various services. I decided to try frigate and I'm so happy with how well it works. I put it on a cloudflare tunnel with cloudflare access controls. Now, I can pull up the cameras with no fuss and the motion tracking worked without even doing anything. Night and day difference from the Dahua nvr

The computer is a Dell Optiplex sff with a 13500 and its running Ubuntu server. Im currently running 5 cameras and its only using about 13% cpu for everything im running

Hi all,

I'm helping a friend of mine with a system. He has a older reolink nvr. I've set up a old laptop for him with home assistant installed. I've imported the nvr through the reolink integration, works a charm. It has multiple cameras, but I want frigate to check only one of those to detect a stork.

How can I find the correct rtsp feed? I've entered rtsp: user:password@ip-nvr/camera05 but it doesn't want to load (in vlc first)

Anyone know how I should set it up?

I'm on my phone now so I can't check the exact link I tried, but it worked on my system at home (without nvr) just not when I fill in his credentials on his system.

https://preview.redd.it/e5z8lrxe66zg1.png?width=1200&format=png&auto=webp&s=c269bca89b7ad8b8fa07d487ee2ae41f811be433

This is just a heads up for those of you using the GenAI function with Frigate. It's extraordinarily powerful (I was able to cut my "notification spam" to absolutely zero...I only get alerts for threat level 1 and 2 now, and they're 100% accurate and I can tell at a glance whether I need to watch the video or not). I've saved all my notifications for the past three days and made a short video (at bottom of the post). The times when it's actually me/my wife/both of us getting in the car, we almost never get notifications about that because part of my prompt is "if [my name] or [wifes name] are recognized, which you'll know if they're passed along to you in the object list, no matter what is happening in the scene it's threat level 0". So in the video the few that there are of us is when it didn't recognize our faces or we deliberately hid them.

Anyway, back to the main point of the post...with a well crafted prompt, I'm able to use "dumber" models to get additional speed while not losing out on the details that I'm looking for. I've got an absolutely MASSIVE prompt and am currently using gemma 4 26b A4B on an AMD MI60 GPU with 32gb VRAM. I have 130k context (two parallel slots, each with their own 130k context window). My current Frigate metrics show that I'm getting my review summaries as:

Average Inference Time 50722.53ms
(and it would be about 15 seconds less than that if I didn't allocate the 1024 thinking tokens, but I've found that without any thinking at all with this model, even with a good prompt it can get details incorrect)

I'll save my entire prompt for another post, as I had to modify a core python file (frigate/genai/__init__.py ) to have my prompt come after the "analyses guidelines" (which I also had to modify, as a number of the elements of it were interfering with my mechanical rules that I wanted to be followed strictly, and there were too many references to allowing it to "think" about the scene versus just following precisely what I told it to do...it would talk itself out of things being a threat level and assigning them a 0 that I wanted to be threat level 2).

Just a quick example, if it were the middle of the day and I ran out to get something out of the car but my face was hidden, it would assign threat level 0 because "this appears to be a homeowner retrieving something from your car" ...and while that's technically correct, my prompt says that any time ANY unknown person at all EVER opens or interacts with my car that it HAS to be a threat level 2. We park in a townhouse parking lot with shared parking and on a street where plenty of people walk their dogs and such. We've had our car broken into twice and my wife can occasionally forget to lock the doors. If someone that the facial recognition doesn't recognize opens a car door, I don't want the AI "guessing" if that's a problem...its ALWAYS a problem.

Anyway, like I said, that's for a different post.

          ====================================================================
          VEHICLE DOOR SIDE CLASSIFICATION — U.S. DRIVER STANDARD
          DETERMINISTIC RULE SET (NO HEURISTICS)
          ====================================================================

          None of what follows is "relative" to anything.  It's to be interpreted only as written explicitly. No reasoning or thinking.  Follow in the order given:

          STEP 1
          1 - can you see the headlights and/or grill - if so, that means the vehicle is "front facing" - stop - otherwise
          2 - can you see the taillights and/or the trunk - if so, that means the vehicle is "rear facing" - stop 
          Now you now whether its front of rear facing
          END STEP 1

          STEP 2
          1 - Divide the entirety of the viewable frame perfectly down a dividing line that is dictated by the center of the viewable vehicle (to be done per vehicle).  That should give you two "rectangles" that bewteen the two are the entirety of the frame. 
          2 - Is the person or object of interest in the left rectangle? If so, then they're "left side" - stop - otherwise
          3 - Is the person or object of interest in the right rectangle? - then they're "right side" - stop
          Now you now whether they're on the "left side" or "right side"
          END STEP 2

          STEP 3
          1 - "Front facing" combined with "left side" = passenger side - stop - otherwise
          2 - "Front facing" combined with "right side" = drivers side - stop - otherwise
          3 - "Rear facing" combined with "left side" is drivers side - stop - otherwise
          4 - "Rear facing" combined with "right side" is passengers side - stop
          END STEP 3

          STEP 4
          OUTPUT FORMAT (MANDATORY):
          - Use EXACTLY ONE of the following:
          - "Driver-side door"
          - "Passenger-side door"
          - "Door side undetermined"

          OUTPUT ORDER (MANDATORY — follow this sequence exactly):
          1. Resolve vehicle orientation and door side first (silent or shown) using the steps above
          2. Explain to yourself how you arrived at the determination to be sure, mechanically going through them
          3. Write scene description using the resolved door side label after the above step

          Do NOT write the scene description before door side is resolved (if it's relevant to the scene)
          The scene description MUST use the door side label produced by the 
          classification steps above — never an independently assumed label.
          END STEP 4

So that's just one part of my very long prompt about vehicle door side classification. You might think that even that is absurdly long for one thing...and I've tried many many iterations and I finally realized I should just upload the screenshots of things it was getting wrong to my localLLM and ask it to tell me why it came to the conclusion that it did. This helped me immensely in writing my prompt to get the results I wanted. For example, when I first had the prompt I had the front/rear facing part working (it identifying the front/rear of the car) and it just would CONTINUOUSLY say "driver side". So I uploaded it and asked it to tell me why, and it would say "well the person is on the right side of the frame"...which is technically correct...I am on the right side of the frame, but not the right side of the car. There's more to it than that, but that's a quick summary.

So, if you're not getting the results you want from your prompt (or don't have a very good one to begin with), try deliberately doing some things on your camera, taking some screenshots of them, uploading them to your LLM of choice and have it explain how it's coming the incorrect conclusions that it is.

https://reddit.com/link/1t3qcf8/video/zz8i50v2z5zg1/player

Need some camera suggestions for a camera that will be facing open acreage in the back of the my house. Key requirements will be decent nightvision (I'm open to lighting suggestions in addition to the camera) main requirements is must be able to function without the cloud (I don't do cloud based services), relatively wide field of view, and ability to properly identify creatures at distance. Price is relatively subjective here but I think PTZ is out due to its introduced latency.

I had my frigate instance shutdown for ~7-8 months due to relocations. I've brought it back up, and I've deployed some cameras. I moved from 0.15, to 0.17.

I'm trying to get generative AI working for reviews, summaries, objects, etc.
I read the documentation, I added the sections.

I put my config into their helper AI bot (and it does guide me to add wrong config at times)

But, absolutely nothing is logged in frigate, and there are no called made to my openAI compatible API endpoint. What else do I have to do?

environment_vars:
  OPENAI_BASE_URL: https://open-webui.host/api/chat/completions

genai:
  provider: openai
  api_key: key
  model: Qwen3-VL-8B-Instruct-Q4_K_M.gguf
  provider_options:
    context_size: 10240 

mqtt:
  enabled: true
  host: mqtt.host
  port: 1883
  user: user
  password: password

logger:
  default: info
  logs:
    frigate.genai: debug

semantic_search:
  enabled: true
  reindex: false

auth:
  failed_login_rate_limit: 1/second;5/minute;20/hour
  trusted_proxies:
    - network/24

go2rtc:

  streams:
    Driveway:
      - ffmpeg:rtsp://192.168.88.20:554/h264?username=grde&password=hgdedd#video=copy#audio=aac

    Driveway_sub:
      - ffmpeg:rtsp://192.168.88.20:554/h264cif?username=hfdn&password=jfdfrg#video=copy#audio=aac

record:
  enabled: true
  motion:
    days: 30
  alerts:
    retain:
      days: 30
      mode: motion
  detections:
    retain:
      days: 30
      mode: motion

cameras:

  Driveway:
    enabled: true
    detect:
      enabled: true
      width: 640
      height: 360
      fps: 5
    snapshots:
      enabled: true
    ffmpeg:
      output_args:
        record: preset-record-generic-audio-copy
      hwaccel_args: preset-intel-qsv-h264
      inputs:
        - path: rtsp://0.0.0.0:8554/Driveway 
          input_args: preset-rtsp-restream
          roles:
            - record

        - path: rtsp://0.0.0.0:8554/Driveway_sub 
          input_args: preset-rtsp-restream
          roles:
            - audio
            - detect
    motion:
      mask: 0.647,0.901,0.976,0.899,0.978,0.957,0.649,0.954
    objects:
      track:
        - person
        - cat
        - dog
        - bicycle
        - bear
        - horse
        - sheep
        - car
        - truck
        - bear
        - cow
        - horse

      filters:
        person: {}
    zones: {}

detectors:
  coral:
    type: edgetpu
    device: usb

model:
  labelmap_path: /labelmap.txt
  width: 300
  height: 300
  path: /models/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite

review:
  genai:
    enabled: true

objects:
  genai:
    enabled: true
    use_snapshot: true

  track:
    - person
    - cat
    - dog
    - bicycle
    - bear
    - horse
    - sheep
    - car
    - truck
    - bear
    - cow
    - horse


version: 0.17-0

Hello everyone !

Have been running Frigate NVR for a couple of years now, and although it generally works, -and-is-kinda-great I would argue, I do have a couple issues that annoy me, that I am pretty sure are my mistake rather than actual problems. This is why I would like your help !

I have 3 main issues !

1. The UI always shows me "[camera-name] has high detect CPU usage (193%)". Now, for the cameras that do show the high-cpu usage warnings, I do have the main stream set to detect, rather than the sub stream. But since they are outside and have quite a big field of view, if I set the detect stream to the low-res, the detections aren't all that great. My question is why do I get 200% CPU usage, when my actual CPU, is mostly sleeping ? (will explain the setup later). And could I possibly improve this somehow ?
2. When first loading the web UI with all the camera views, frigate most of the time, loads pretty fast. At least on local network. Less than 1s. But then, when clicking a camera to open it for full single view, most camera feeds take more than 1-2s to load, and some might even take 4-5+ seconds. This, is on my local network as well.
3. Sometimes, recordings are lost. Meaning for a specific time-range, maybe even a couple of hours, for SOME cameras, I might be able to see the low-res "timelapse-like" preview when seeking the timeline, but when actually clicking there is no video available. This is weird. 😃 For me, the most important thing 100% is a full 24/7 record of what is going on. No matter how good detections might have gotten, I still can't rely on them.

Anyway, I took the time to ask, and I hope someone might have some input to share with me, on (possibly) a wrong config, or something I could improve.

My Setup:

Running Frigate (currently 0.17.1) in Docker, on Unraid.
The machine is a Dual Xeon 2690v2 | 128GB RAM | 1660 Ti and I have both a dual TPU module and a dedicated SSD only for Frigate. Nothing else access it.

My Frigate Config:
https://pastebin.com/QPZgVwfA

My Cameras are all TP-Link Tapo, but one that is a Dahua Dual lens camera.

Any suggestions are welcome.

I'm looking for advice about minimal GPU setup for frigate.
I have mini itx case with x8 pcie slot.
I would like to use gpu for:

I'm using coral for object detection

embeddings (enrichments)

build in face recognition large model

maybe gen ai description with ollama (some small model) etc.

I'm looking on RTXA2000
What is minimal GPU for it?
Single slot and short form factor is preferred

Once again new to frigate and for whatever reason I cannot get the microphone icon to show up for porch_1 but it works for porch_2, its doing the opposite of what I would like

            rtsp://127.0.0.1:8554/porch_1
          input_args: preset-rtsp-restream
          roles:
            - record
        - path: 
            rtsp://127.0.0.1:8554/porch_2
          input_args: preset-rtsp-restream
          roles:
            - detect

>

Just starting out with Frigate and by default it uses the standard rtsp path, when should i choose the standard rtsp vs the restream?

What happens if rtsp path 127.0.0.1 is used but preset-rtsp-restream args isn's added?

            rtsp://127.0.0.1:8554/porch_1
            # rtsp://{FRIGATE_USER}:{FRIGATE_PASSWORD}@IP:554/cam/realmonitor?channel=1&subtype=0&unicast=true&proto=Onvif
          input_args: preset-rtsp-restream
          roles:
            - record
            # - detect
        - path: 
            rtsp://127.0.0.1:8554/porch_2
            # rtsp://{FRIGATE_USER}:{FRIGATE_PASSWORD}@IP:554/cam/realmonitor?channel=1&subtype=1&unicast=true&proto=Onvif
          input_args: preset-rtsp-restream
          roles:
            - detect

Hello community!

Quick question to you. I’m about to upgrade to Frigate + for improved animal detection. Looks like Badger is not a specified ? How do you guys make sure you get alerts for badgers? Do you “tweak” detection for dog or foxes ? Usually it appear when it is dark so that add complexity, any suggestions from someone with experience?

Started state classification for a garage door, some images where it's partly open or closed (as in taken while it's moving) appear in the non-100% classifications, is it best to just ignore them?

Also am I right in assuming the model is comparing grey-scale image? There are a variety in the closed state that look very similar but for varying levels of brightness.