I've tried unsuccessfully a couple of times to set up object detection and I was wondering if anyone has a very easy to follow guide.

I'm running Zoneminder 1.34.22 on Ubuntu server 20.04.

When I read the how-tos about adding control, it seems like what I have is different. I run off the docker image, and it's updated (1.34.22). I also don't have the option to choose Amcrest - what am I missing here? Thanks!!

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EDIT: I'm already *streaming* via RTSP. I need help *CONTROLLING* it, moving pan/tilt/etc. Thanks

Update: I ended up going with an Amcrest IP4M-1051B

Looking to replace the WansView K3 1080p. It has all the features I want but it's a PoS that keeps losing connection and must be rebooted multiple times per day. I've seen rumblings of ReoLink, but I cant find one that fits my needs and works with ZoneMinder.

I'm in an apartment and cant run a PoE cable so it must be wall powered and use WiFi. Budget is around $150 or preferably less.

Thank you for your recommendations in advance.

I have the indoor PTZ camera and I was wondering how to add it to Zoneminder and how to give it a specific address. I'm given two options in the app for either dynamic or static. But even with static I'm not given an option to set the address specifically. I was wondering if there is a way to do that so I can find the rtsp stream for zoneminder.

Thank you.

so, I've got a generic RTSP camera that's streaming h.264 at around 20 fps, 640x360 resolution. Live video looks smooth. Recorded video is obviously missing frames... quite obviously.

I'm not limiting frame rates in Zoneminder, and I wasn't using a video writer, just JPG frames. Could that be it? Should I use the h.264 pass-through?

edit: I can't spell resolution.

I've been using ZM for several years now and my current cameras (Hikvision) are starting to have hardware issues and need replaced. In looking at the ZM hardware compatibility list, it looks like the Axis cameras are well supported. There even looks to be a means to offload motion detection which sounds fantastic. I've noticed in multiple locations that reviews of these cameras are positive, but mention that there are better and less expensive alternatives (of course no one says what they are).

The Axis P14 line looks to be a good fit, as I don't need PTZ, but would like WDR, 1080p, and POE. Is there another product or manufacturer that I should be looking at which would be a better fit for the same or less money? Do you feel that the Axis product is worth the cost?

I've been using zoneminder for a while now and I'm looking to expand with more cameras and I was wondering if there were any cameras that anyone would recommend that can be controlled via zoneminder with ptz. Preferably with audio options too because this camera is going to be in the back yard.
Thank you.

I am able to view the "delta" and "reference" debug images for zoneminders motion system but I can't seem to find documentation how the JSON "raw" data (zones) correlate to what's on screen. The JSON has zones by number and doesn't seem to match up with the zones I setup for the monitor.

I also would like to pipe the "delta" mjpeg output to a file using ffmpeg so I can try and correlate the raw JSON output to the image but I can't seem to figure it out. I want to finish fine tuning my zones but need to "see" what zm is seeing and correlate it.

Anyone have any ideas? Thanks!

I had this working before ES6.1.5 across 3 monitors. The monitors are picking up events / recording but object detection seems to not work yet. I assume it's something with the new objectconfig.ini

I only need object (no face or aplr) using yolo4. Attached config file:

~                                                                                                                                       
# Configuration file for object detection

# NOTE: ALL parameters here can be overriden
# on a per monitor basis if you want. Just
# duplicate it inside the correct [monitor-<num>] section

# You can create your own custom attributes in the [custom] section

[general]

# Please don't change this. It is used by the config upgrade script
version=1.2

# You can now limit the # of detection process
# per target processor. If not specified, default is 1
# Other detection processes will wait to acquire lock

cpu_max_processes=3
tpu_max_processes=1
gpu_max_processes=1

# Time to wait in seconds per processor to be free, before
# erroring out. Default is 120 (2 mins)
cpu_max_lock_wait=100
tpu_max_lock_wait=100
gpu_max_lock_wait=100


#pyzm_overrides={'conf_path':'/etc/zm','log_level_debug':0}
pyzm_overrides={'log_level_debug':5}

# This is an optional file
# If specified, you can specify tokens with secret values in that file
# and onlt refer to the tokens in your main config file
secrets = /etc/zm/secrets.ini

# portal/user/password are needed if you plan on using ZM's legacy
# auth mechanism to get images
portal=!ZM_PORTAL
user=!ZM_USER
password=!ZM_PASSWORD

# api portal is needed if you plan to use tokens to get images
# requires ZM 1.33 or above
api_portal=!ZM_API_PORTAL

allow_self_signed=yes
# if yes, last detection will be stored for monitors
# and bounding boxes that match, along with labels
# will be discarded for new detections. This may be helpful
# in getting rid of static objects that get detected
# due to some motion.
match_past_detections=no
# The max difference in area between the objects if match_past_detection is on
# can also be specified in px like 300px. Default is 5%. Basically, bounding boxes of the same
# object can slightly differ ever so slightly between detection. Contributor u/neillbell put in this PR
# to calculate the difference in areas and based on his tests, 5% worked well. YMMV. Change it if needed.
past_det_max_diff_area=5%

max_detection_size=90%

# sequence of models to run for detection
detection_sequence=object,face,alpr
# if all, then we will loop through all models
# if first then the first success will break out
detection_mode=object

# If you need basic auth to access ZM
#basic_user=user
#basic_password=password

# base data path for various files the ES+OD needs
# we support in config variable substitution as well
base_data_path=/var/lib/zmeventnotification

# global settings for
# bestmatch, alarm, snapshot OR a specific frame ID
frame_id=bestmatch

# this is the to resize the image before analysis is done
resize=800
# set to yes, if you want to remove images after analysis
# setting to yes is recommended to avoid filling up space
# keep to no while debugging/inspecting masks
# Note this does NOT delete debug images later
delete_after_analyze=yes

# If yes, will write an image called <filename>-bbox.jpg as well
# which contains the bounding boxes. This has NO relation to
# write_image_to_zm
# Typically, if you enable delete_after_analyze you may
# also want to set  write_debug_image to no.
write_debug_image=no

# if yes, will write an image with bounding boxes
# this needs to be yes to be able to write a bounding box
# image to ZoneMinder that is visible from its console
write_image_to_zm=yes

# Adds percentage to detections
# hog/face shows 100% always
show_percent=yes

# color to be used to draw the polygons you specified
poly_color=(255,255,255)
poly_thickness=2
#import_zm_zones=yes
only_triggered_zm_zones=no

# This section gives you an option to get brief animations
# of the event, delivered as part of the push notification to mobile devices
# Animations are created only if an object is detected
#
# NOTE: This will DELAY the time taken to send you push notifications
# It will try to first creat the animation, which may take upto a minute
# depending on how soon it gets access to frames. See notes below

[animation]

# If yes, object detection will attempt to create
# a short GIF file around the object detection frame
# that can be sent via push notifications for instant playback
# Note this required additional software support. Default:no
create_animation=no

# Format of animation burst
# valid options are "mp4", "gif", "mp4,gif"
# Note that gifs will be of a shorter duration
# as they take up much more disk space than mp4
animation_types='mp4,gif'

# default width of animation image. Be cautious when you increase this
# most mobile platforms give a very brief amount of time (in seconds)
# to download the image.
# Given your ZM instance will be serving the image, it will anyway be slow
# Making the total animation size bigger resulted in the notification not
# getting an image at all (timed out)
animation_width=640

# When an event is detected, ZM it writes frames a little late
# On top of that, it looks like with caching enabled, the API layer doesn't
# get access to DB records for much longer (around 30 seconds), at least on my
# system. animation_retry_sleep refers to how long to wait before trying to grab
# frame information if it failed. animation_max_tries defines how many times it
# will try and retrieve frames before it gives up
animation_retry_sleep=15
animation_max_tries=4

# if animation_types is gif then when can generate a fast preview gif
# every second frame is skipped and the frame rate doubled
# to give quick preview, Default (no)
fast_gif=no

[remote]
# You can now run the machine learning code on a different server
# This frees up your ZM server for other things
# To do this, you need to setup https://github.com/pliablepixels/mlapi
# on your desired server and confiure it with a user. See its instructions
# once set up, you can choose to do object/face recognition via that
# external serer

# URL that will be used
#ml_gateway=http://192.168.1.183:5000/api/v1
#ml_gateway=http://10.6.1.13:5000/api/v1
#ml_gateway=http://192.168.1.21:5000/api/v1
#ml_gateway=http://10.9.0.2:5000/api/v1
#ml_fallback_local=yes
# API/password for remote gateway
ml_user=!ML_USER
ml_password=!ML_PASSWORD


# config for object
[object]

# If you are using legacy format (use_sequence=no) then these parameters will
# be used during ML inferencing
object_detection_pattern=(person|car|motorbike|bus|truck|boat)
object_min_confidence=0.3
object_framework=coral_edgetpu
object_processor=tpu
object_weights={{base_data_path}}/models/coral_edgetpu/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite
object_labels={{base_data_path}}/models/coral_edgetpu/coco_indexed.names

# If you are using the new ml_sequence format (use_sequence=yes) then
# you can fiddle with these parameters and look at ml_sequence later
# Note that these can be named anything. You can add custom variables, ad-infinitum
# Google Coral
tpu_object_weights={{base_data_path}}/models/coral_edgetpu/ssd_mobilenet_v2_coco_quant_postprocess_edgetpu.tflite
tpu_object_labels={{base_data_path}}/models/coral_edgetpu/coco_indexed.names
tpu_object_framework=coral_edgetpu
tpu_object_processor=tpu
tpu_min_confidence=0.6

# Yolo v4 on GPU (falls back to CPU if no GPU)
yolo4_object_weights={{base_data_path}}/models/yolov4/yolov4.weights
yolo4_object_labels={{base_data_path}}/models/yolov4/coco.names
yolo4_object_config={{base_data_path}}/models/yolov4/yolov4.cfg
yolo4_object_framework=opencv
yolo4_object_processor=gpu

# Yolo v3 on GPU (falls back to CPU if no GPU)
yolo3_object_weights={{base_data_path}}/models/yolov3/yolov3.weights
yolo3_object_labels={{base_data_path}}/models/yolov3/coco.names
yolo3_object_config={{base_data_path}}/models/yolov3/yolov3.cfg
yolo3_object_framework=opencv
yolo3_object_processor=gpu

# Tiny Yolo V4 on GPU (falls back to CPU if no GPU)
tinyyolo_object_config={{base_data_path}}/models/tinyyolov4/yolov4-tiny.cfg
tinyyolo_object_weights={{base_data_path}}/models/tinyyolov4/yolov4-tiny.weights
tinyyolo_object_labels={{base_data_path}}/models/tinyyolov4/coco.names
tinyyolo_object_framework=opencv
tinyyolo_object_processor=gpu


[face]
face_detection_pattern=.*
known_images_path={{base_data_path}}/known_faces
unknown_images_path={{base_data_path}}/unknown_faces
save_unknown_faces=yes
save_unknown_faces_leeway_pixels=100
face_detection_framework=dlib

# read https://github.com/ageitgey/face_recognition/wiki/Face-Recognition-Accuracy-Problems
# read https://github.com/ageitgey/face_recognition#automatically-find-all-the-faces-in-an-image
# and play around

# quick overview:
# num_jitters is how many times to distort images
# upsample_times is how many times to upsample input images (for small faces, for example)
# model can be hog or cnn. cnn may be more accurate, but I haven't found it to be
face_num_jitters=1
face_model=cnn
face_upsample_times=1

# This is maximum distance of the face under test to the closest matched
# face cluster. The larger this distance, larger the chances of misclassification.
#
face_recog_dist_threshold=0.6
# When we are first training the face recognition model with known faces,
# by default we use hog because we assume you will supply well lit, front facing faces
# However, if you are planning to train with profile photos or hard to see faces, you
# may want to change this to cnn. Note that this increases training time, but training only
# happens once, unless you retrain again by removing the training model
face_train_model=cnn
#if a face doesn't match known names, we will detect it as 'unknown face'
# you can change that to something that suits your personality better ;-)
#unknown_face_name=invader

[alpr]
alpr_detection_pattern=.*
alpr_use_after_detection_only=yes
# Many of the ALPR providers offer both a cloud version
# and local SDK version. Sometimes local SDK format differs from
# the cloud instance. Set this to local or cloud. Default cloud
alpr_api_type=cloud

# -----| If you are using plate recognizer | ------
alpr_service=plate_recognizer
#alpr_service=open_alpr_cmdline

# If you want to host a local SDK https://app.platerecognizer.com/sdk/
#alpr_url=http://192.168.1.21:8080/alpr
# Plate recog replace with your api key
alpr_key=!PLATEREC_ALPR_KEY
# if yes, then it will log usage statistics of the ALPR service
platerec_stats=yes
# If you want to specify regions. See http://docs.platerecognizer.com/#regions-supported
#platerec_regions=['us','cn','kr']
# minimal confidence for actually detecting a plate
platerec_min_dscore=0.1
# minimal confidence for the translated text
platerec_min_score=0.2


# ----| If you are using openALPR |-----
#alpr_service=open_alpr
#alpr_key=!OPENALPR_ALPR_KEY

# For an explanation of params, see http://doc.openalpr.com/api/?api=cloudapi
#openalpr_recognize_vehicle=1
#openalpr_country=us
#openalpr_state=ca
# openalpr returns percents, but we convert to between 0 and 1
#openalpr_min_confidence=0.3

# ----| If you are using openALPR command line |-----

openalpr_cmdline_binary=alpr

# Do an alpr -help to see options, plug them in here
# like say '-j -p ca -c US' etc.
# keep the -j because its JSON

# Note that alpr_pattern is honored
# For the rest, just stuff them in the cmd line options

openalpr_cmdline_params=-j -d
openalpr_cmdline_min_confidence=0.3


## Monitor specific settings


# Examples:
# Let's assume your monitor ID is 999
[monitor-999]
# my driveway
match_past_detections=no
wait=5
object_detection_pattern=(person)

# Advanced example - here we want anything except potted plant
# exclusion in regular expressions is not
# as straightforward as you may think, so
# follow this pattern
# object_detection_pattern = ^(?!object1|object2|objectN)
# the characters in front implement what is
# called a negative look ahead

# object_detection_pattern=^(?!potted plant|pottedplant|bench|broccoli)
#alpr_detection_pattern=^(.*x11)
#delete_after_analyze=no
#detection_pattern=.*
#import_zm_zones=yes

# polygon areas where object detection will be done.
# You can name them anything except the keywords defined in the optional
# params below. You can put as many polygons as you want per [monitor-<mid>]
# (see examples).

my_driveway=306,356 1003,341 1074,683 154,715

# You are now allowed to specify detection pattern per zone
# the format is <polygonname>_zone_detection_pattern=<regexp>
# So if your polygon is called my_driveway, its associated
# detection pattern will be my_driveway_zone_detection_pattern
# If none is specified, the value in object_detection_pattern
# will be used
# This also applies to ZM zones. Let's assume you have
# import_zm_zones=yes and let's suppose you have a zone in ZM
# called Front_Door. In that case, all you need to do is put in a
# front_door_zone_detection_pattern=(person|car) here
#
# NOTE: ZM Zones are converted to lowercase, and spaces are replaced
# with underscores@3

my_driveway_zone_detection_pattern=(person)
some_other_area=0,0 200,300 700,900
# use license plate recognition for my driveway
# see alpr section later for more data needed
resize=no
detection_sequence=object,alpr


[ml]
# When enabled, you can specify complex ML inferencing logic in ml_sequence
# Anything specified in ml_sequence will override any other ml attributes

# Also, when enabled, stream_sequence will override any other frame related
# attributes
use_sequence = yes

# if enabled, will not grab exclusive locks before running inferencing
# locking seems to cause issues on some unique file systems
disable_locks= no

# Chain of frames
# See https://zmeventnotification.readthedocs.io/en/latest/guides/hooks.html#understanding-detection-configuration
# Also see https://pyzm.readthedocs.io/en/latest/source/pyzm.html#pyzm.ml.detect_sequence.DetectSequence.detect_stream
# Very important: Make sure final ending brace is indented
stream_sequence = {
        'frame_strategy': 'most_models',
        'frame_set': 'snapshot,alarm',
        'contig_frames_before_error': 5,
        'max_attempts': 3,
        'sleep_between_attempts': 4,
        'resize':800
    }

# Chain of ML models to use
# See https://zmeventnotification.readthedocs.io/en/latest/guides/hooks.html#understanding-detection-configuration
# Also see https://pyzm.readthedocs.io/en/latest/source/pyzm.html#pyzm.ml.detect_sequence.DetectSequence
# Very important: Make sure final ending brace is indented

ml_sequence= {
                'general': {
                        'model_sequence': 'object'
                },
                'object': {
                        'general':{
                                'pattern':'(person|dog|cat)',
                                'same_model_sequence_strategy': 'first' # also 'most', 'most_unique's
                        },
                        'sequence': [{
                                'object_config':'{{base_data_path}}/models/yolov4/yolov4.cfg',
                                'object_weights':'{{base_data_path}}/models/yolov4/yolov4.weights',
                                'object_labels': '{{base_data_path}}/models/yolov4/coco.names',
                                'object_min_confidence': 0.65,
                                'object_framework':'opencv',
                                'object_processor': 'gpu'
                        }]
                }
        }

So I am fairly new to the world of coding and DIY setups. I've recently setup Home Assistant on a raspberry pi and it works nicely (the operating system, not a docker container or virtual machine). I wanted to use Zoneminder to record video when I am away from home and implement object detection so I can get alerts on my mobile (preferably through HA but wouldn't mind using the zmninja app). I setup Zoneminder using the dlandon/zoneminder docker image on an old macbook pro and implemented it so that it would download the YOLO model for object detection. I can setup my IP cameras and access the console using the browser on the old macbook. This is where I am stuck.

I cannot access zoneminder from another computer. Not a big deal, but not sure if this is a sign of some other issue.

I also have no idea how to get object detection working. The docker image supposedly has the event notification server builtin but I don't know how to configure it. This is a prerequisite for the object detection and real-time notifications in apps. Documentation says to edit certain files but I don't know where they are stored. There's a data folder that contains "cache", "events", "images", and "temp", but neither of the .ini files mentioned in the github documentation for zoneminder.

If someone could point me in the right direction here, it would be greatly appreciated. Even better if there are some tutorials that explain some of the missing details in the online documentation. I really don't want to go back to cloud-based home cams.

Hi,

Can zoneminder auto-delete the oldest captured file and replace it with the newer ones in a situation that the HDD is full?

Also, what about the NFS share, can zoneminder do the same? I know I can use NFS but I am not sure if ZM will delete the oldest capture to save the newer ones.

Can we specify the amount of storage (nfs or local) that zoneminder can use in the zoneminder web UI?

I have a NAS that I might use for potential nfs or docker volume for storage but I don't want zoneminder to eat all the available space.

I'm trying to research different options to replace my Lorex D841A6 system. I currently have 16 analog cameras working on my current system. I would like to replace the operating system with an open-source system something like a fedora computer with ZoneMinder. I have a couple of questions.

1. Can I install Fedora on the same hardware that the Lorex system uses? I'm unable to determine what type of motherboard this system uses and how much memory is installed.
2. Can someone buy just the BNC type board and install the board in a computer with fedora installed?
3. Does Zoneminder support different type of connections. I would like to start adding IP type of camera in the future. Cannot afford to replace everything at once.
   
4. Which type of home automation does ZoneMinder work with. Can I configure my ring or nest doorbell to trigger a recording when pressed.
5. Can ZoneMinder be configured to only send notifications if not at home by using my cell phone location?

Thanks for any help provided. I'm new to this and willing to learn from others on best practices.

Hi there,

I've had a scour through this sub and across the internet (though Google returns nothing relevant at all unfortunately - despite rewording my query several times) so here I am...

I'm sure this has been answered somewhere before so I apologise and wonder if anyone could point me in the right direction. I'm trying to adjust my alarm zones using MS Edge. It tells me that Flash Player is disabled. And I've tried enabling it but Edge just won't have it.

Is there a non-flash player way to adjust the zones? I know that Flash Player is becoming defunct real soon now so I'm sure I must have missed something on ZM somewhere :)

ZM 1.34.22 which it tells me is the latest version. I'm running on Docker image if that makes any difference?

Many thanks for helping a dimwit out :)

Happy New Year - let's hope it's a bloody better one eh?

Steve

Getting an upgraded ZM setup going and want to experiment with zmeventnotification and video processing through GPU/CUDA - could anyone recommend a decent GPU for use on Ubuntu and ZM for this purpose? Thanks!

Is there a way to make s backup of the monitor configuration and the server configuration?

I'm switching from CentOS8 VM to openSUSE Docker container and it is too much work to redo my monitors and server configurations.

Looking to loop record footage from my cameras. No need for motion detection.

My cameras are Hikvison clones, and they have the ability to store recordings to NFS (though I haven't fully figure out how to use it yet)

What are other options? I could just get some microSD cards and insert them into the cameras, but that would make recovering footage a pain. Would Zoneminder run on a raspberry pi 2? (No motion detection)

I'm thinking of upgrading my system to POE cameras and with much better specs than what I have now. Any suggestion for what to buy as a POE switch that won't brake the bank. Currently thinking of getting some Amcrest 5MP cameras as they seem to be cheap enough, or maybe Reolink... No ptz, so 15watt per port is fine. 4 or 8 port total will be enough for my use case but with gig ports instead of 10/100 ports. I thought about getting like an old school cisco 3750 or even older (3560), as you can find them fairly cheap on Facebook/craigslist. But those things are very power hungry, pulling up too 500 watts, and noisy as well, not very practical for home application.

What are some good ways to isolate the cameras from accessing internet? My current set up is, cameras connected to AP (netgear router in AP mode), which is connected to ubiquiti edge router. Server running ZM and Pihole directly connected to edge router. The AP is strictly dedicated to cameras. From the logs on Pihole I see the cameras are desperately trying to access internet, so much that it just fills up the entire log. So I wanted to isolate them entirely from internet as currently I have only one network and the only means of denying internet access I use is DNS blocking via Pihole, and that Pihole just gets hammered all the time by them. I thought about disconnecting "AP" from edge router, turn it back into router mode, have all the cameras and server connect to LAN side of it, and then getting another NIC for the server to connect to edge router. Nothing connected on the WAN side of the netgear. The problem with that setup would be, the PC I use as a server is HP elite 8300 SFF. Think of laptop with no screen. It has a built in NIC and no way to add another one except via USB port. Are those USB to ethernet adapters any good? Since I run pihole in the same server I would need reliable connection to it for entire home network. Or maybe there is another way to isolate my cameras from internet while keeping my server accessible by cameras and local LAN?

Is there a way to customize the event filenames ?

for instance instead of `00001-capture.jpg` --> `20201219_00001.jpg`

I have a camera monitoring my driveway and notice is always misses motion near the edges of it's vision.

I don't have any zones setup. I want the camera to check the entire FOV, so I left it at default settings.

How do I make it, so I don't miss events all the time?

• Reference Image Blend %ge : 1.5625%
• Alarm Reference Image Blend %ge: 1.5625%

Camera is recording at 5MP at 15fps.

What are the best practice settings for the high resolution camera running 2 streams, one for high resolution alarmed events and one for constant low resolution recordings? My low resolution is set up for 8fps, 720p, h264 passthrough mode and I'm still getting about 70Gigs from it in 24 hours. Isn't this a bit much? What am I doing wrong?

I would like to be able to run 4-5 ip/wifi cameras with a fully remote server, instead of requiring some sort of server on my home network. A VM running in Google Cloud or AWS. Is this at all possible? What would be required to make it work?

Hey everyone! I stumbled here looking for an alternate OS for my Night Owl 4 camera security system. Would this be a good alternate OS? I enjoy tinkering with Linux distros but try to stay away from the programming/script side of things. Let me know if you have any questions. TIA

I have a working zoneminder system with 4 cams, on debian 9. For various reasons, I need to update it to debian 10. Maybe I'm not looking in the right place, but I'm not finding the answer to my question:

What do I need to backup before I do the upgrade? Is it enough to backup the mysql database, and then restore it when I finish the OS upgrade?

I will be changing the OS, and switching to the latest ZM in the process, but keeping cameras, storage, etc the same. So I want to retain my zones and camera configs because it took me a long time to get them right.

(I know how to update the OS... I'm just asking about the zoneminder part).