Address Recognition and Analytics Reference Implementation

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

 

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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 2020 Release

 

 

Recommended Hardware

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

• ADLINK* MXE-5500 Series

• NEXCOM* VTC 7252-7C4IP

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 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 local dashboard, and a cloud dashboard.

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:

• Text Recognition 14
• Horizontal Text Detection 001

 

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

 

2. Go to the address_recognition_and_analytics.zip/ directory.

   cd address_recognition_and_analytics.zip/

 

3. Change permission of the executable edgesoftware file.

   chmod 755 edgesoftware

 

4. Run the command below to install the Reference Implementation.

   ./edgesoftware install

 

5. 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.
   
   

6. 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/Address_Recognition_And_Analytics_2022.1/output.log

 

7. 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_2022.1/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase

 

Step 2: Run the Application

Prerequisites

• Set Up ThingsBoard* Local Cloud Data
• Set Up Amazon Web Services* Cloud Storage

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_2022.1/IEdgeInsights REPO_FOLDER=<INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase 

   
   For example: 

   make webui EII_BASE=/home/intel/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/IEdgeInsights REPO_FOLDER=/home/intel/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase

 

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

3. 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 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* Local Cloud Data to enable all dashboard features, including the cloud storage.
   
   
   
   NOTE: If you don't have an AWS account, you can still enable the ThingsBoard Cloud Data.
    
4. Access the Address Recognition and Analytics 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.
     NOTE: These images are ONLY to be used for validating the accuracy of detection events.
     
     
   • Click on the Browse button and search for video on the following path:

     <INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase/config/VideoIngestion/test_videos/

     
      
   • 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 address plaques on the surfaces of buildings:
     NOTE: These images are ONLY to be used for validating the accuracy of detection events.
     
     

5. 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.
   
   

6. 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.
   
   

 

Run in Parallel with Work Zone Analytics Reference Implementation

 

NOTE: Check the Edge Insights for Fleet Release Notes Known Limitation section to confirm the compatibility matrix for parallel execution.

 

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

Prerequisites

• Two terminals

• Follow the steps to install Work Zone Analytics after installing Address Recognition and Analytics

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_2022.1/Work_Zone_Analytics/EII-WorkZoneDetection-UseCase

   
   
   

 

2. Change directory to Address Recognition and Analytics path on terminal 2:

   cd <INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase

   
   
   

 

3. 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_2022.1/IEdgeInsights REPO_FOLDER=<INSTALL_PATH>/work_zone_analytics/Work_Zone_Analytics_2022.1/Work_Zone_Analytics/EII-WorkZoneDetection-UseCase

   
   For example: 

   make webui EII_BASE=<INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/IEdgeInsights REPO_FOLDER=/home/intel/work_zone_analytics/Work_Zone_Analytics_2022.1/Work_Zone_Analytics/EII-WorkZoneDetection-UseCase

 

4. 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_2022.1/IEdgeInsights REPO_FOLDER=<INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/Address_Recognition_And_Analytics/EII-AddressDetection-UseCasemake webui EII_BASE=<INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/IEdgeInsights REPO_FOLDER=<INSTALL_PATH>/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase

   
   For example: 

   make webui EII_BASE=/home/intel/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/IEdgeInsights REPO_FOLDER=/home/intel/address_recognition_and_analytics/Address_Recognition_And_Analytics_2022.1/Address_Recognition_And_Analytics/EII-AddressDetection-UseCase

   
   
   

 

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

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

7. Wait for Visualizer to get up and running.

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

9. Configure all available cameras with the desired videos and set the target for each one (CPU or GPU) and click Run Use Case.
   NOTE: These images are ONLY to be used for validating the accuracy of detection events.
   
   

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.

 

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:

• Visit Edge Insights for Fleet for additional reference implementations.
• Intel® Distribution of OpenVINO™ toolkit documentation

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'

 

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

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

 

3. 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”.

 

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