MaRMOT

Modular and Reconfigurable Multiple Object Tracking (MaRMOT) framework for robots and intelligent systems in ROS2. MaRMOT is designed to be fast and accurate enough for real-time applications, modular, reconfigurable, and capable of running on embedded hardware.

This package is under active development as part of my Ph.D. in robotics at UT Austin--if there is a feature you would like to see, please contact me or raise an issue!

Updates

• 26 May 2024: our MaRMOT paper was accepted to the 33rd IEEE International Conference on Robot and Human Interactive Communication (IEEE RO-MAN 2024)! To reproduce the results of the paper, check out the RO-MAN 2024 release.

About

MaRMOT is an open-source ROS2 implementation of the Tracking-by-Detection paradigm, used by many common trackers with deep learning-based LiDAR and vision detectors. However, MaRMOT can be extended for an arbitrary detector type.

There are a few key components to know:

• Track and Detection objects, implemented in marmot/datatypes.py
• Detection pre-processing, implemented in the /src/*_preproc.cpp files. This converts an arbitrary detectionfrom one ROS2 message type into the MaRMOT tracking_msgs/Detections3D.msg format for processing by the tracker node.
• The tracker node, implemented in marmot/tracker.py. It inherits the marmot/assignment.py and marmot/management.py modules, and publishes tracking_msgs/Tracks3D.msg objects for general tracking use and Foxglove foxglove_msgs/SceneUpdate.msg messages for visualization in Foxglove Studio.

Supporting file types include:

• ROS2 /launch files, which start the nodes, specify configuration files, and play back data as needed.
• /config files, which specify the tracker parameters, detector parameters, and object parameters.

Usage

Installation and Setup

To install MaRMOT, follow the installation instructions.

Recreating IEEE RO-MAN 2024 results

If interested in recreating the nuScenes and Motion Capture/MoCap experimental results from our 2024 MaRMOT paper, check out the RO-MAN 2024 release and follow the usage instructions.

Running nuScenes experiments

During development, we used the nuScenes tracking development kit for tuning and evaluation. The results are reported in our 2024 paper.

If interested in using the nuScenes dev kit for tracker development, see the nuScenes instructions.

Running the OAK-D demo

After completing the installation instructions, if you have a Luxonis OAK-D camera you can use the included files to run MaRMOT with an OAK-D detector.

If using OAK-D for the first time, set up udev rules for the device:

echo 'SUBSYSTEM=="usb", ATTRS{idVendor}=="03e7", MODE="0666"' | sudo tee /etc/udev/rules.d/80-movidius.rules
sudo udevadm control --reload-rules && sudo udevadm trigger

Then:

cd ~/tracking_ws
conda activate marmot
source install/setup.bash
ros2 launch marmot oakd_demo.launch.py

Now in a separate window, open up foxglove-studio to visualize.

foxglove-studio

Then, connect to the default connection at ws://localhost:8765 to view live tracker data.

Development

If interested in adding a new model, method, or feature (or if the existing code could be better modified for your purposes) please see the development instructions, raise an issue, or contact me.

Acknowledgements

The nuScenes .mcap conversion script is a modified version of the original from Foxglove, available here. While the original Foxglove version uses protobuf serialization, the included file uses Foxglove's ROS2 serialization, with the same datatypes.

The tracking evaluation script is copied from Nutonomy's nuScenes devkit repo here for convenience.

MaRMOT uses a modified version of Weng et al's AB3DMOT greedy matching algorithm, found here.

MaRMOT uses PyTorch3D to compute 3D Intersection-over-Union (IoU).

Potential Improvements

Usability

• Docker/containerized version

Readability

• Move detector initialization to separate class
• Move process models to separate class
• Move yaw correction to separate utility class