This sample implements libraries for video content analysis, database ingestion, content search and visualization:

• Ingest: Analyze video content and ingest the data into the VDMS.
• VDMS: Store metadata efficiently in a graph-based database.
• Visualization: Visualize content search based on video metadata.

**This is a concept sample in active development.**

Software Stacks

This sample is powered by the following software stacks:

• NGINX Web Service:
  • The NGINX/FFmpeg-based web serving stack is used to store and segment video content and serve web services. The software stack is optimized for Intel Xeon Scalable Processors.

License Obligations

• FFmpeg is an open source project licensed under LGPL and GPL. See https://www.ffmpeg.org/legal.html. You are solely responsible for determining if your use of FFmpeg requires any additional licenses. Intel is not responsible for obtaining any such licenses, nor liable for any licensing fees due, in connection with your use of FFmpeg.

Install Prerequisites:

• Time Zone: Check that the timezone setting of your host machine is correctly configured. Timezone is used during build.

• Build Tools: Install cmake and m4 if they are not available on your system.

• Docker Engine:

  • Install docker engine and verify you have Docker Compose V2 2.18.0+ setup.
  • Setup docker proxy as follows if you are behind a firewall:

    sudo mkdir -p /etc/systemd/system/docker.service.d
    printf "[Service]\nEnvironment=\"HTTPS_PROXY=$https_proxy\" \"NO_PROXY=$no_proxy\"\n" | sudo tee /etc/systemd/system/docker.service.d/proxy.conf
    sudo systemctl daemon-reload
    sudo systemctl restart docker

Prepare Videos and/or Camera Configurations

This application processes videos and camera streams by watching the inputs directory. MP4 videos should be placed in this directory and the application will automatically begin processing it. If interested in using sample videos, see examples.

To configure the application for camera streams, add the camera name and URL to inputs/camera_config.yaml. The YAML file provides a sample entry.

Start/Stop Service using Scripts:

We have provided scripts to help with the deployment. The start_app.sh script provides everything you need to deploy the service. To run the service using GPU, use:

./start_app.sh -e GPU

This same script can be used to run the service with CPU, resizing video to 640x640, etc. The accepted parameters for the start script are below:

Parameter	Type	Description	Default
-h	optional	Print this help message
-d or --debug	optional	Flag for debugging	"0"
-e or --device	optional	Device to use for inference	CPU
-i or --ingestion	optional	Ingestion type (object, face)	"object,face"
-l or --tars	optional	Flag to load docker images instead of building from Dockerfiles	"0"
-m or --model	optional	Custom YOLO model name (.pt). If not provided model YOLO11n is used.
-o or --omit-det	optional	By default, object detections are printed. To omit printing detections to screen, enable flag.	False
-z or --resize	optional	Flag to resize video to model input size (640x640)	False

See also Customize Build Process for additional information on options.

The stop_app.sh script is provided to stop the service from a different terminal. To stop and prune the docker images (data doesn't persist), run:

./stop_app.sh -p

NOTE: Remove -p if you only want to stop the docker containers without pruning docker builder, containers, volumes, and networks.

Manually Build Streaming Sample:

Instead of running the start/stop using the above scripts, you can run the commands individually as shown below.

mkdir build
cd build
cmake ..
make

See also Customize Build Process for additional options.

Start/Stop Sample:

Use the following commands to start/stop services via docker-compose:

make start_docker_compose
make stop_docker_compose

Display Object Detected

Once the application is started, we have provided a script to display the detections with associated timestamps. To use the script, be sure NOT to use flags -o or --omit-det so the detection details are available. To display this information, in a separate terminal, run display_detections.sh with the objects of interest. For example, to display when "person" and "car" objects are detected, run:

./display_detections.sh person,car

Launch Sample UI:

Launch your browser and browse to https://<hostname>:30007. The sample UI is similar to the following:

NOTE: If you see a browser warning of self-signed certificate, please accept it to proceed to the sample UI.

Redeploy Service by Persisting Data

There may be cases where you want to stop the service but redeploy later with data persisted. In such cases, follow the following steps, assuming service is currently running, i.e. via ./start_app.sh -e CPU -z -d:

1. [Optional] For redeployment, we will save the state of the named volumes. To conserve space in the lcc_vdms-content volume, it may be beneficial to delete the /mnt/tmp directory, in case some files were not removed. This is done by running:

   docker exec -it lcc_vdms-service_1 rm -rf /mnt/tmp

   
   

2. Save docker images for running service:

   ./deployment/DockerImageTars/save_docker_images.sh -e CPU -z

   
   

   The options used should be the same as those used for running the original service, if available. The accepted parameters for save_docker_images.sh are below:
   

   Parameter	Type	Description	Default
   -h	optional	Print this help message
   -e or --device	optional	Device to use for inference	CPU
   -z or --resize	optional	Flag to resize video to model input size (640x640)	False

   
   

3. Stop the running service:

   ./stop_app.sh

   The stop script stops the service, removes all stopped Docker containers and removes all unused Docker networks. We DO NOT use the -p flag here to avoid removing the volumes before backing them up.
   

4. Backup the docker volumes to redeploy later. First rename the lcc_app-content volume and remove original:

   ./deployment/DockerImageTars/rename_volume.sh lcc_app-content lcc_app-content_saved
   
   docker volume rm lcc_app-content

   The lcc_vdms-content volume is sparse, so to backup, compress the volume to respective location. In this case, we save the file to deployment/DockerImageTars/resize and remove original:

   docker run --rm -v lcc_vdms-content:/data \
     -v ${PWD}/deployment/DockerImageTars/resize:/backup \
     ubuntu bash -c "tar czSf /backup/lcc_vdms-content_saved.tar.gz /data"
   
   docker volume rm lcc_vdms-content

   
   

5. At this point, images and volumes are saved. You can safely, prune all docker images/volumes if needed via ./stop_app.sh -p.

6. Prior to redeployment, videos already inserted in previous deployment, which should not be processed again (if any), should be removed from inputs/, and any camera configurations should be updated for next deployment.

7. Repopulate the docker volumes to expected name. For lcc_app-content_saved, we can simply rename it:

   ./deployment/DockerImageTars/rename_volume.sh lcc_app-content_saved lcc_app-content
   
   docker volume rm lcc_app-content_saved

   The lcc_vdms-content volume should be created and populated with compressed data from previous step:

   docker volume create lcc_vdms-content
   
   docker run --rm -v lcc_vdms-content:/data \
     -v ${PWD}/deployment/DockerImageTars/resize:/backup \
     ubuntu bash -c "tar -xvzf /backup/lcc_vdms-content_saved.tar.gz"

   
   

8. Restart the service with appropriate options but include --tars to use saved images. Here, we're re-deploying using the same flags as the original deployment.

   ./start_app.sh -e CPU -z -d --tars

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See Also

• Configuration Options

• Visual Data Management System