Drivetrack

Installation

# Initialize the git submodules
git submodule init --update

# Install dependencies
conda env create -f environment.yml
conda activate drivetrack
pip install -r requirements.txt --no-dependencies

# Build NLSPN DeformConv
cd nlspn/src/model/deformconv
sh make.sh
cd ../../../../

# Build CompletionFormer DeformConv
cd completionformer/src/model/deformconv
sh make.sh
cd ../../../../

Note that the depdencies that ship with the Waymo SDK need to be upgraded, which is why we install using pip and the --no-dependencies flag.

Getting Started

First, obtain access to the Waymo Open Dataset if you don't have access already.

An example of generating DriveTrack (requires access to the Waymo Open Dataset in GCS):

python generate_drivetrack.py \
  --output-dir "output-path" \
  --use-gcsfs \
  --split "training" \
  --version "1.0.0" \
  --gpus 0,1,2

Alternatively, if you have the Waymo dataset downloaded locally, you can specify a local path instead of using the GCS bucket:

python generate_drivetrack.py \
  --output-dir "output-path" \
  --local-dataset-path "dataset-path" \
  --split "training" \
  --version "1.0.0" \
  --gpus 0,1,2

A full list of arguments can be found in generate_drivetrack.py.

Attribution

DriveTrack was made using the Waymo Open Dataset, provide by Waymo LLC under license terms available at waymo.com/open.

Citation

If you use this code or our data for your research, please cite:

DriveTrack: A Benchmark for Long-Range Point Tracking in Real-World Videos
Arjun Balasingam, Joseph Chandler, Chenning Li, Zhoutong Zhang, Hari Balakrishnan.
To appear at CVPR 2024

@inproceedings{balasingam2024drivetrack,
 author = {Arjun Balasingam and Joseph Chandler and Chenning Li and Zhoutong Zhang and Hari Balakrishnan},
 title = {DriveTrack: A Benchmark for Long-Range Point Tracking in Real-World Videos},
 booktitle = {CVPR},
 year = {2024}
}