Address Recognition and Analytics Reference Implementation

Version: 2022.2   Published: 03/23/2022  

Last Updated: 10/23/2022

Overview

Based on Intel® Edge Insights for Fleet (EIF) framework, Address Recognition and Analytics Reference Implementation delivers DL models, computer vision algorithms, OpenVINO™ toolkit, Edge2Cloud connectivity, and other software dependencies that will detect address plaques on the surfaces of buildings and records the addresses on the plaques.

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

Configure & Download

NOTE: This software package will not work on the People's Republic of China (PRC) network.

Legal Disclaimers
Recipient is solely responsible for compliance with all applicable regulatory standards and safety, privacy, and security related requirements concerning Recipient's use of the Intel hardware and software.
Recipient is solely responsible for any and all integration tasks, functions, and performance in connection with use of the Intel hardware or software as part of a larger system. Intel does not have sufficient knowledge of any adjoining, connecting, or component parts used with or possibly impacted by the Intel hardware or software or information about operating conditions or operating environments in which the Intel hardware or software may be used by Recipient. Intel bears no responsibility, liability, or fault for any integration issues associated with the inclusion of the Intel hardware or software into a system. It is Recipient’s responsibility to design, manage, and assure safeguards to anticipate, monitor, and control component, system, quality, and or safety failures.

 


  • Time to Complete:  Approximately 60 minutes
  • Programming Language:  Python*
  • Available Software:  Intel® Distribution of OpenVINO™ toolkit 2021.4.2 Release

 

Recommended Hardware

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


Target System Requirements

  • Ubuntu* 20.04 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 camera that is mounted on the side of a last mile delivery vehicle. The camera is connected to an in-vehicle Edge PC based on the Intel® Core™ processor or Intel Atom® processor. When the vehicle stops, it detects address plaques on the surfaces of buildings and records the addresses on the plaques.

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

This application is intended to be further developed for eventual use by fleet managers to validate that deliveries were made to a pre-determined address. It is not intended for use in employee performance evaluation.

OpenVINO™ Models used for this Reference Implementation include:

 

Architecture Diagram

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.

NOTE: The images provided in the reference implementation are ONLY to be used for validating the accuracy of detection events.

 

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.

 

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

 

  1. Go to the address_recognition_and_analytics.zip/ directory.
    cd address_recognition_and_analytics.zip/

 

  1. Change permission of the executable edgesoftware file.
    chmod 755 edgesoftware

 

  1. Run the command below to install the Reference Implementation.
    ./edgesoftware install

 

  1. 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.

    Product Key

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

    Installation Success

    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/Address_Recognition_And_Analytics_2022.2/output.log

 

  1. 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>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.2/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase

 

Step 2: Run the Application

Prerequisites

  1. Run the application:
    Copy and run the make webui command from the end of the installation log: 
    make webui EII_BASE=<INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_<version>/IEdgeInsights REPO_FOLDER=<INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_<version>/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase 

    For example: 
    make webui EII_BASE=/home/intel/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.2/IEdgeInsights REPO_FOLDER=/home/intel/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.2/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase

 

  1. Open the Web UI: Go to 127.0.0.1:9097 on your web browser.

    Open Web GUI

  1. If you installed your ThingsBoard 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 complete the Cloud configuration, make sure you click on the Save Credentials and Save Token buttons. Now you can import the ThingsBoard dashboard as described at the end of the Set Up ThingsBoard* Cloud Data to enable all dashboard features, including the cloud storage.

    AWS Configuration

    NOTE: If you don't have an AWS account, you will not be able to access Storage Cloud. You can still enable the ThingsBoard Cloud Data if you configured it locally or on another machine.
  2. Access the Address Recognition and Analytics Dashboard with the following steps. 
    • Go to sidebar and select Run Use Case.

      Run Use Case
    • Configure the use case by selecting the video sample and the device for the UDF model. Enter a target address value (for example, 30) to generate the target address reached alert. 

      Optionally, you can also set the simulation data that you want to use. You can choose between using the KnowGo Simulator or simply use the CSV pre-recorded simulation data.

      Model Descriptions
      Address Detection: This model detects the numbers on a address plaque.
      Address Recognition: This model recognizes the target address and sends an alert when the address number is reached.

      NOTE: These images are ONLY to be used for validating the accuracy of detection events.

      RI Dashboard
    • Click on the Browse button and search for video on the following path:
      <INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.2/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase/config/VideoIngestion/test_videos/

       
    • After selecting the video sample, select the target device for all models. Options include CPU or GPU. Click on Run Use Case.
      NOTE: To use a GPU device, you must set the proper group for the device with the command: 
      sudo chown root:video /dev/dri/renderD128

       
    • The application will start the Visualizer App that will detect address plaques on the surfaces of buildings:
      NOTE: These images are ONLY to be used for validating the accuracy of detection events.

      RI Visualizer

  1. After the visualizer starts, you can go to the ThingsBoard link and check the alerts sent by the reference implementation. If you configured the AWS credentials, you will also have access to video snapshots taken by the application on the video stream.

    ThingsBoard Dashboard with Data

  1. You can also check the cloud storage from the Reference Implementation Storage tab.
    NOTE: These images are ONLY to be used for validating the accuracy of detection events.

    Check Storage


 

Run in Parallel with Work Zone Analytics Reference Implementation

 

To run this task you will need to download and install the Work Zone Analytics Reference Implementation.

Prerequisites

Steps to Run the Application

  1. Change directory to Work Zone Analytics Use Case path on terminal 1:
     
    cd <INSTALL_PATH>/work_zone_analytics/Work_Zone_Analytics_2022.2/Work_Zone_Analytics/EII-WorkZoneDetection-UseCase


    Change Directory

 

  1. Change directory to Address Recognition and Analytics path on terminal 2:
    cd <INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.2/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase


    Change Directory, second screen

 

  1. Run the following command 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>/address_recognition_and_analytics/Address_Recognition_And_Analytics_<version>/IEdgeInsights REPO_FOLDER=<INSTALL_PATH>/work_zone_analytics/Work_Zone_Analytics_<version>/Work_Zone_Analytics/EII-WorkZoneDetection-UseCase

    For example: 
    make webui EII_BASE=<INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.2/IEdgeInsights REPO_FOLDER=/home/intel/work_zone_analytics/Work_Zone_Analytics_2022.2/Work_Zone_Analytics/EII-WorkZoneDetection-UseCase

 

  1. 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:
    make webui EII_BASE=<INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_<version>/IEdgeInsights REPO_FOLDER=<INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_<version>/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase 

    For example: 
    make webui EII_BASE=/home/intel/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.2/IEdgeInsights REPO_FOLDER=/home/intel/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.2/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase


    Webserver Application

 

  1. Open your browser and go to 127.0.0.1:9096.

  2. Configure Work Zone Analytics by setting the video source, the target and click on Run Use Case.

  3. Wait for Visualizer to get up and running.

  4. Open the Address Recognition and Analytics page by going to address 127.0.0.1:9097.

  5. Configure all available cameras with the desired videos and set the target for each model. Options include CPU or GPU. Click Run Use Case.
    NOTE: These images are ONLY to be used for validating the accuracy of detection events.

    Configure WZA

At this point Work Zone Analytics will close and after that both use cases will start.

NOTE: These images are ONLY to be used for validating the accuracy of detection events.

Two Use Cases in Parallel

 

NOTE: If you reinstall the first reference implementation, you must also reinstall the second one.

 

Summary and Next Steps

On a last-mile delivery van, a camera is mounted on the side of the vehicle. The camera is connected to an in-vehicle Edge PC based on the Intel® Core™ processor or Intel Atom® processor. When the vehicle stops, it detects address plaques on the surfaces of buildings and records the addresses on the plaques.

As a next step extend the reference implementation to:

  • Collate the address plaque detection outputs with the navigation SW result to detect driver or mapping errors.
  • Provide reports and evidence to delivery fleet managers to review if there are package delivery issues.

Learn More

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

Known Issues

Uninstall Reference Implementation

If you uninstall one of the reference implementations, 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.

  1. Get DNS server using the command:
    nmcli dev show | grep 'IP4.DNS'

 

  1. Configure Docker to use the server. Paste the line below in the /etc/docker/daemon.json file:
    { "dns": ["<dns-server-from-above-command>"]}

 

  1. 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 retrying to install if group docker is available for you by running the following command in a terminal:
 

groups

The output should contain “docker”.

 

Support Forum

If you're unable to resolve your issues, contact the Support Forum.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.