In the realm of autonomous driving, understanding how to track multiple objects in a three-dimensional space is crucial. Here, we delve into the methods used in the award-winning probabilistic 3D multi-object tracking system developed for the NuScenes Tracking Challenge. This straightforward guide will walk you through the entire implementation process, including troubleshooting tips for a smooth experience.

Step-by-Step Implementation Guide

To reproduce the results from the validation set, follow these steps:

• Set Up Your Working Environment

  First, create a directory where you will work. Navigate to this directory in your terminal:

  cd YOUR_WORKING_DIR

  Then, clone the tracking code:

  git clone https://github.com/eddyhkchiu/mahalanobis_3d_multi_object_tracking

  Next, set up your conda environment:

  conda create --name probabilistic_tracking python=3.6

  Activate the environment:

  conda activate probabilistic_tracking

  Navigate to the project directory:

  cd mahalanobis_3d_multi_object_tracking

  Install required packages:

  pip install -r requirements.txt

• Download the NuScenes Devkit

  Create another directory:

  cd YOUR_NUSCENES_DEVKIT_DIR

  Download the nuScenes devkit:

  git clone https://github.com/nutonomy/nuscenes-devkit.git

  Install its requirements:

  cd nuscenes-devkit; pip install -r setup/requirements.txt

• Include NuScenes Devkit in Python Path

  Set the Python path to include your devkit:

  export PYTHONPATH=$PYTHONPATH:YOUR_NUSCENES_DEVKIT_DIR/nuscenes-devkit/python-sdk

• Download NuScenes Data

  Select a directory for data storage:

  cd YOUR_NUSCENES_DATA_DIR

  Download and unzip the mini, trainval, and test data from the NuScenes download webpage. Note that you will need to create an account.

• Download and Prepare MEGVII Detection Results

  Create a directory for the detection results:

  cd YOUR_NUSCENES_DETECTION_DIR

  Then download and unzip the MEGVII detection results from the NuScenes tracking webpage.

• Edit Code for Custom Directories

  In the files main.py, evaluate_nuscenes.py, and get_nuscenes_stats.py, replace the placeholders with the paths to your data directories.

• Run Tracking Code

  Execute the tracking code, which takes about 15 minutes:

  python main.py val 2 m 11 greedy true nuscenes results000008

  To evaluate:

  python evaluate_nuscenes.py --output_dir results000008 results000008/val/results_val_probabilistic_tracking.json results000008/output.txt

• Run All Methods (Optional)

  You can run the implementation of the AB3DMOT method as well:

  python main.py val 0 iou 0.1 h false nuscenes results000001

  Or execute all tracking methods in one go by using the script:

  source run.sh

• View Estimates for Kalman Filter Covariance Matrices (Optional)

  You can also run this script if needed:

  python get_nuscenes_stats.py nuscenes_train_stats.txt

Understanding the Code with an Analogy

Think of the tracking code as a GPS navigator in a bustling city. Just as a GPS uses various signals (like satellites) to pinpoint your location, this code leverages multiple inputs (like object detection results) to track vehicles and pedestrians in a 3D space. The packets of data sent from the devices are like the GPS signals telling the system where each object is, while timestamps are akin to GPS reading the journey history. This system ensures that, even as objects move quickly or change direction suddenly, it keeps track of their paths as smoothly as a GPS guiding you through twists and turns.

Troubleshooting Tips

If you encounter issues during implementation, consider the following solutions:

• Environment Issues: Ensure the proper version of Python and all dependencies are installed. Sometimes, package versions conflict, leading to unexpected behavior. Use conda list to check your installed packages.
• Permissions Error: Make sure you have the necessary permissions to access directories and files, especially when downloading large datasets.
• Path Errors: Verify that all your paths in code match the directories where you have placed your data and code. Even minor discrepancies can cause errors.
• Performance Slowness: If tracking takes longer than expected, consider optimizing your environment or running on a more robust machine.

For further assistance, engage with the AI community or check out the detailed resources available at NuScenes. For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Implementing a probabilistic 3D multi-object tracking system can significantly enhance capabilities in autonomous vehicles. By following the outlined steps, you can successfully reproduce the validation set performance and explore this exciting aspect of AI.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.