Public Transit Analytics Reference Implementation

Version: 2022.2   Published: 12/09/2021  

Last Updated: 12/13/2022

Overview

Public Transit Analytics (PTA) reference implementation demonstrates how to use Edge Insights for Fleet middleware and delivers deep learning models, computer vision algorithms, OpenVINO™ and other software. In this example, the model outputs counts of passengers on public transport, potentially for use in public transportation route planning. The results are available for the bus driver and the bus fleet operators via a cloud dashboard. The application also temporarily stores relevant video images for validating the accuracy of detections.

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

Configure & Download

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

Recommended Hardware

The below hardware is recommended for use with this reference implementation. For other suggestions, see Recommended Hardware.

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 IP cameras mounted inside a bus in the passenger area with an Intel® Core™ or Intel Atom® processor-based computer onboard the bus. Pretrained models are used to inference and calculate the number of passengers.

This reference implementation contains a notification subsystem which includes a cloud dashboard and a cloud storage for the bus operator and fleet manager.

The architecture is represented by a complex block diagram.

Figure 1: 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 public_transit_analytics.zip

  2. Go to the public_transit_analytics/ directory.

    cd public_transit_analytics/

  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.

    A console window showing a system prompt to enter the product key.

    Figure 2: Product Key

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

    A console window showing system output during the install process. At theend of the process, the system displays the message “Installation of packagecomplete” and the installation status for each module.

    Figure 3: Installation Success

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

  7. To start the application, change the directory using the cd command printed at the end of the installation process:

    cd /opt/intel/eif/EII-UseCaseManager/

Step 2: Run the Application

Prerequisites

  1. Run the application. Copy and run the make webui command from the end of the installation:

    make webui

  2. Open the Web UI: Go to 127.0.0.1:9090 on your web browser.

    A browser window showing the reference implementationdashboard.

    Figure 4: Reference Implementation Dashboard

  3. 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 the 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.

    A web app dashboard showing the Configuration tab. Certain fields arecovered with a blue bar for security

    Figure 5: Configuration Tab Contents

    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.

  4. Access the Public Transit Analytics Dashboard with the following steps.

    • Go to sidebar and select the Run Application menu option.

      A web app dashboard showing the Run Application menu option.

      Figure 6: Select Run Application Menu Option

    • Configure the use case by selecting the video sample and the device for the inference model.

    • 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 Description

    • Face Detection: Face detector based on ResNet152 as a backbone with a ATSS head for indoor and outdoor scenes shot by a front-facing camera. Select video sample and the CPU or GPU device for the inference model to run on it.

      A web app dashboard showing the Dashboard.

      Figure 7: Configure Use Case

    • Click on the Browse button and search for the sample video delivered with the application at the following path: <INSTALL_PATH>/public_transit_analytics/Public_Transit_Analytics_2022.1/Public_Transit_Analytics/EII-PassengerCounting-UseCase/config/VideoIngestion/test_videos/ and select the one available.

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

      Dashboard showing the sample video search results.

      Figure 8: Search for Sample Video

    • After selecting the video sample, select the device for the inference model. Options include CPU or GPU. Click on Run Application.

    • The application will start the Visualizer App that detects people and counts them as in the following image:

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

      A web app dashboard showing output from the visualizer.

      Figure 9: Visualizer Output

  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 pictures taken by the application on the video stream.

    A browser window showing the ThingsBoard link with the Intel Fleet Manager dashboard in the main view. Several components are displayed, including Alerts, Temperature, and a map showing the vehicle location.

    Figure 10: Intel Fleet Manager Dashboard shown in ThingsBoard

  6. You can also check the cloud storage from the Reference Implementation Storage menu option.

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

    A web app dashboard showing the Storage menu option.

    Figure 11: Reference Implementation Storage Menu Option

Run in Parallel with Automated License Plate Recognition Reference Implementation

To run this task you will need to download and install Automated License Plate Recognition Reference Implementation.

For more details about parallel execution, see the Edge Insights for Fleet Use Case Manager documentation.

Prerequisites

Steps to Run the Application

  1. Change directory to EII-UseCaseManager path on your terminal:

    cd /opt/intel/eif/EII-UseCaseManager

  2. Run the following command on your terminal to start the web server application.

    make webui

  3. Open your browser and go to 127.0.0.1:9090.

  4. Configure both installed reference implementations by setting the video source and the target. Click on Run Application.

    NOTE: Configure each reference implementation by selecting the desired tab. For example, click the Run Application menu option, then click on PTA to configure the Public Transit Analytics RI. Next, click on ALPR to configure the Automated License Plate Recognition RI.

    A browser window showing application with ALPR and PTA tabs - PTA selected.

    Figure 12: Configure Public Transit Analytics Reference Implementation

    A browser window showing application with ALPR and PTA tabs - ALPR selected.

    Figure 13: Configure Automated License Plate Recognition Reference Implementation

  5. Wait for both Visualizers to get up and running.

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

    A browser window showing output of 2 visualizers in a side-by-side view.

    Figure 14: Visualizer Output for 2 Reference Implementations

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

Extend the RI further to provide support for feed from network stream (RTSP camera) and optimize the algorithm for better performance.

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'

  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".

Installation Timeout When Using pip or apt Commands

You may experience a timeout issue when using the People's Republic of China (PRC) internet network.

Make sure that you have a stable internet connection while installing the packages. If you experience timeouts due to Linux* apt or Python* pip installation, try to reinstall the package.

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.