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Object Tracking using Yolov8 and FMCW Radar

This project's goal is to create software that can be used to track objects from a UAV using both live radar sensor data and video using Yolo.

Documentation

The project is currently in progress. The best way to view the documentation for this project is to visit the documentation's website. That will have the best formatting, and more information than provided here. This README contains some examples, and high level documentation/examples.

Development and Running Locally

Please see the Development Environment Documentation for more details on running this project locally and developing.

Run in Docker

This project can be fully run and configured from a docker container. Follow the steps below to get the image build and running in a container.

Accessing the Image

Pulling Image from DockerHub

# Linux Image
docker pull nbowness/uav-experiments:latest

# Jetson Jetpack 5 Image
docker pull nbowness/uav-experiments:latest-jetson-jetpack5

Building the Container Locally

# Build conatiner for linux
docker build . -t uav-experiments:latest

# Build container for jetson-jetpack5
docker build . -f Dockerfile-jetson-jetpack5 uav-experiments:latest-jetson-jetpack5

Update Image Tag

You can update the tag to simply the process for running the container locally. The local name tracking-image will be used for all following examples and commands to simplify the documentation.

docker tag nbowness/uav-experiments:latest-jetson-jetpack5 tracking-image

Running the Container On WSL, Linux

Below includes a series of commands that can be used to interact and run the container. For a full list of options, and some quickstart guides please see Running In Docker. Below will show a few examples to get you started.

# Run the container interactively, letting you access the contents
docker run -it tracking-image  

# Run the tracking algorithm with default parameters
docker run -it tracking-image python3 tracking.py

# Interactively launch the container, with configuration, data and output volumes mounted. Please see the configuration section for more details. 
# Once launched you can run commands as you'd like
docker run -v "$(pwd)"/data:/data -v "$(pwd)"/configuration:/configuration -v "$(pwd)"/output:/output -it tracking-image

# Run tracking without radar enabled
docker run -v "$(pwd)"/data:/data -v "$(pwd)"/configuration:/configuration -v "$(pwd)"/output:/output -it tracking-image python3 tracking.py --skip-radar

# Run tracking without video enabled, and a different radar IP
docker run -v "$(pwd)"/data:/data -v "$(pwd)"/configuration:/configuration -v "$(pwd)"/output:/output -it tracking-image python3 tracking.py --skip-video --radar-ip 10.0.1.60

# Run with UI elements active.
xhost +local:docker && docker run -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix -v ~/.Xauthority:/root/.Xauthority -v "$(pwd)"/data:/data -v "$(pwd)"/configuration:/configuration -v "$(pwd)"/output:/output -it tracking-image

# Run the tracking software using local webcam, with no UI elements active
docker run -v "$(pwd)"/data:/data -v "$(pwd)"/configuration:/configuration -it tracking-image python3 tracking.py --skip-radar --view-img --device=/dev/video0:/dev/video0 --video-config /configuration/VideoConfig_AnkerCamera.yaml

Running the Container On Jetson

When running on Jetson. Jetson equips iGPU rather than the dGPU. The OS is also a customized l4t rather than standard Linux. You can use --runtime=nvidia and by default, it will enable GPU for usage. All commands above are still valid as well, just use the jetson base image and include --ipc=host --runtime=nvidia. To help synchronize the start time of the radar and video processing adding a 30 seconds delay to starting the radar is helpful. Yolo takes approx 30 seconds to install on the Jetson.

# Run the container interactively
docker run -v "$(pwd)"/data:/data -v "$(pwd)"/output:/output --ipc=host --runtime=nvidia -it tracking-image

# After being inside the container, you can run the following:
python3 tracking.py --radar-start-delay 30 # Add 30 second delay, since loading the Yolo model on Jetson takes a while

Configuration in the container

Folder Structure

This project uses the Ultrlytics images as the base images for this project. Specifically it uses the normal Dockerfile and the Dockerfile and the Dockerfile-jetson-jetpack5

This project rebuilds a new image that add some additional python projects into the tracking directory. It will also suggests mounting volumes for data, configuration and output to attach precollected data to process, add configuration options and gather output files mounted to your machine respectively.

For more explict details on the folder structure, and the overview of the configuration options please see Configuration of the docs.

Data Processing

flowchart LR
    %% Class definition to allow for bigger icon pictures
    classDef bigNode font-size:24px;

    subgraph tracking["Tracking.py"]
        %%  Radar Processing SubGraph
        subgraph radarProc["**RadarProcess.py**"]
            style desc1 text-align:left
            desc1["`1 Collect data
            2 Data drocessing
            3 Put detection into queue`"]
        end

        %%  Image Processing SubGraph
        subgraph imageProc["`**ImageProcess.py**`"]
            style desc2 text-align:left
            desc2["`1 Collect image
            2 Data processing
            3 Put detection into queue`"]
        end
        Queue[["MP Queue"]]
        subgraph trackProc["`**ObjectTracking.py**`"]
            style desc3 text-align:left
            desc3["`1 Read data from queue
            2 Update tracks based of new data
            3 Report current tracks`"]
        end
    end
    Radar[["Radar"]]:::bigNode
    Video[[Video]]:::bigNode

    Radar <--> radarProc
    Video <--> imageProc
    radarProc --> Queue
    imageProc --> Queue
    Queue -- Data read from Queue --> trackProc
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Helpful Notes