Automated License Plate Recognition Reference Implementation

Overview

Automated License Plate Recognition (ALPR) reference implementation delivers deep learning models, computer vision algorithms, OpenVINO and other software dependencies on the Edge Insights for Fleet middleware architecture as a use case for the public safety fleet industry. Front facing dash camera video streams are analyzed to extract the license plate text strings. The results are transmitted and made available via an in-vehicle local dashboard and a cloud dashboard.

Select Configure & Download to download the reference implementation and the software listed below.

Configure & Download

Table 1
Time to Complete	Approximately 60 minutes
Programming Language	Python*
Available Software	Intel® Distribution of OpenVINO™ toolkit 2020 Release

Recommended Hardware

The below hardware is recommended for use with this reference implementation. See the Recommended Hardware page for other suggestions.

Target System Requirements

Ubuntu* 18.04.3 LTS
6th to 10th Generation Intel® Core™ processors with Intel® Iris® Plus graphics or Intel® HD Graphics

How It Works

The reference implementation contains a full pipeline of analytics on video streams from a front facing dash camera on an Intel Core or Atom processor-based computer onboard the fleet vehicle. Pretrained models are used to inference and extract the license plate information.

This reference implementation contains a notification subsystem which includes a local dashboard, and a cloud dashboard.

Get Started

Step 1: Install the Reference Implementation

Select Configure & Download to download the reference implementation and then follow the steps below to install it.

Configure & Download

NOTE: If the host system already has Docker* images and containers, you might encounter errors while building the reference implementation packages. If you do encounter errors, refer to the Troubleshooting section at the end of this document before starting the reference implementation installation.

Open a new terminal, go to the downloaded folder and unzip the downloaded RI package.

unzip automated_license_plate_recognition.zip

Go to the automated_license_plate_recognition/ directory.

cd automated_license_plate_recognition/

Change permission of the executable edgesoftware file.

chmod 755 edgesoftware

Run the command below to install the Reference Implementation.

./edgesoftware install

During the installation, you will be prompted for the Product Key. The Product Key is contained in the email you received from Intel confirming your download.

When the installation is complete, you see the message "Installation of package complete" and the installation status for each module.

NOTE: If you encounter any issues, please refer to the Troubleshooting section at the end of this document. Installation failure logs will be available at the path: /var/log/esb-cli/Automated_License_Plate_Recognition_2021.1/output.log

In order to start the application, you need to change the directory using the cd command printed at the end of the installation process:

cd <INSTALL_PATH>/automated_license_plate_recognition/Automated_License_Plate_Recognition_2021.1/Automated_License_Plate_Recognition/EII-LicensePlateRecognition-UseCase

Step 2: Run the Application

Pre-requisites

Run the application:

Copy and run the make webui command from the end of the installation log:

make webui EII_BASE=<INSTALL_PATH>/automated_license_plate_recognition/Automated_License_Plate_Recognition_2021.1/IEdgeInsights REPO_FOLDER=<INSTALL_PATH>/automated_license_plate_recognition/Automated_License_Plate_Recognition_2021.1/Automated_License_Plate_Recognition/EII-LicensePlateRecognition-UseCase

E.g.:

make webui EII_BASE=/home/intel/automated_license_plate_recognition/Automated_License_Plate_Recognition_2021.1/IEdgeInsights REPO_FOLDER=/home/intel/automated_license_plate_recognition/Automated_License_Plate_Recognition_2021.1/Automated_License_Plate_Recognition/EII-LicensePlateRecognition-UseCase

Open the Web UI: Go to 127.0.0.1:9095 on your web browser.

If you installed your Thingsboard Local Cloud Server and you have enabled S3 Bucket Server on your AWS account you can provide your configured AWS Access Key ID, AWS Secret Access Key, Thingsboard IP, Thingsboard Port and Thingsboard Device token on Cloud Data Configuration tab After you completed the Cloud configuration make sure you click on Save Credentials and Save Token buttons. Now you can import the Thingsboard dashboard as described at the end of the Thingsboard setup to enable all dashboard features which include the cloud storage.

NOTE: If you don't have an AWS account you can still enable the Thingsboard Cloud Data

Access the Automated License Plating Recognition Dashboard with the following steps.

Go to sidebar and select Run Use Case.

Configure the use case. Select video sample and the CPU or GPU device to run on it.

Click on the Browse button and search for one of the sample videos delivered with the application at the following path: <INSTALL_PATH>/automated_license_plate_recognition/Automated_License_Plate_Recognition_2021.1/Automated_License_Plate_Recognition/EII-LicensePlatingRecognition-UseCase/config/VideoIngestion/test_videos/ and select one of the two available.

After selecting the video sample, select the target CPU or GPU and click on Run Use Case.
The application will start the Visualizer App that will detect license plates and license plate numbers following image:

After the visualiser started you can go to Thingsboard Link and check the alerts sent by the reference implementation. If you configured the AWS credentials you will have access also to pictures taken by the application on the video stream

You can also check the cloud storage from the Reference Implementation Storage tab

Run in Parallel with Public Transit Analytics Reference Implementation

To run this task you will need to download and install Public Transit Analytics Reference Implementation.

Pre-requisites

Two terminals
Follow the steps to install Public Transit Analytics after installing Automated License Plating Recognition

Steps to Run the Application

Change directory to Public Transit Analytics Use Case path on terminal 1:

cd <INSTALL_PATH>/public_transit_analytics/Public_Transit_Analytics_2021.1/Public_Transit_Analytics/EII-PassengerCounting-UseCase

Change directory to Automated License Plate Recognition Use Case path on terminal 2:

cd <INSTALL_PATH>/automated_license_plate_recognition/Automated_License_Plate_Recognition_2021.1/Automated_License_Plate_Recognition/EII-LicensePlateRecognition-UseCase

Run the following common on terminal 1 to start the webserver application:

Copy and run the make webui command from the end of the installation log:

make webui EII_BASE=<INSTALL_PATH>/automated_license_plate_recognition/Automated_License_Plate_Recognition_2021.1/IEdgeInsights REPO_FOLDER=<INSTALL_PATH>/public_transit_analytics/Public_Transit_Analytics_2021.1/Public_Transit_Analytics/EII-PassengerCounting-UseCase

E.g.:

make webui EII_BASE=/home/intel/automated_license_plate_recognition/Automated_License_Plate_Recognition_2021.1/IEdgeInsights REPO_FOLDER=/home/intel/public_transit_analytics/Public_Transit_Analytics_2021.1/Public_Transit_Analytics/EII-PassengerCounting-UseCase

Run the following command on terminal 2 to start the webserver application:

Copy and run the make webui command from the end of the installation log:

E.g.:

Open your browser and go to 127.0.0.1:9094.
Configure Public Transit Analytics by setting the video source, the target and click on Run Use Case.
Wait for Visualizer to get up and running.
Open the Automated License Plate Recognition page by going to address 127.0.0.1:9095.
Configure all available cameras with the desired videos and set the target for each one (CPU or GPU) and click Run Use Case.

At this point Public Transit Analytics will close and after that both use cases will start.

NOTE: If you reinstall the first RI, you must reinstall the second one.

Summary and Next Steps

This application successfully implements Intel® Distribution of OpenVINO™ toolkit plugins to calculate the number of passengers.

As a next step, try the following:

It can be extended further to provide support for feed from network stream (RTSP camera), and the algorithm can be optimized for better performance.

Learn More

To continue your learning, see the following guides and software resources:

Intel® Distribution of OpenVINO™ toolkit documentation

Known Issues

Uninstall use case known issue

If you uninstall one of the use cases, you need to reinstall the other ones because the Docker images will be cleared.

Troubleshooting

Installation Failure

If the host system already has Docker images and its containers running, you will have issues during the RI installation. You must stop/force stop existing containers and images.

To remove all stopped containers, dangling images, and unused networks:

sudo docker system prune --volumes

To stop Docker containers:

sudo docker stop $(sudo docker ps -aq)

To remove Docker containers:

sudo docker rm $(sudo docker ps -aq)

To remove all Docker images:

sudo docker rmi -f $(sudo docker images -aq)

Docker Image Build Failure

If Docker image build on corporate network fails, follow the steps below.

Get DNS server using the command:

nmcli dev show | grep 'IP4.DNS'

Configure Docker to use the server. Paste the line below in the /etc/docker/daemon.json file:

{ "dns": ["<dns-server-from-above-command>"]}

Restart Docker:

sudo systemctl daemon-reload && sudo systemctl restart docker

Installation Failure Due to Ubuntu Timezone Setting

While building the reference implementation, if you see /etc/timezone && apt-get install -y tzdata && ln -sf /usr/share/zoneinfo/${HOST_TIME_ZONE} /etc/localtime && dpkg-reconfigure -f noninteractive tzdata' returned a non-zero code: 1 make: *** [config] Error 1

Run the following command in your terminal:

sudo timedatectl set-local-rtc 0

Installation Encoding Issue

While building the reference implementation, if you see ERROR: 'latin-1' codec can't encode character '\\u2615' in position 3: ordinal not in range(256)

Run the following command in your terminal:

export LANG=en_US.UTF-8

Can't Connect to Docker Daemon

If you can't connect to docker daemon at http+docker://localhost, run the following command in your terminal:

sudo usermod -aG docker $USER

Log out and log back in to Ubuntu.

Check before retry to install if group docker is available for your user:

In a terminal, run the following command: groups

The output should contain “docker”.

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Automated License Plate Recognition Reference Implementation

Overview

Target System Requirements

How It Works

Get Started