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README.md

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+# Spatial-Temporal Multi-Cuts for Online Multiple-Camera Vehicle Tracking
+![arXiv Badge](https://img.shields.io/badge/Paper-arXiv.0000.0000-b31b1b.svg)
+[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)
+[![CI](https://github.com/fubel/stmc/actions/workflows/main.yml/badge.svg)](https://github.com/fubel/stmc/actions/workflows/main.yml)
+
+## Authors
+
+[Fabian Herzog](https://github.com/fubel), [Johannes Gilg](https://github.com/Blueblue4), [Philipp Wolters](https://github.com/phi-wol), [Torben Teepe](https://github.com/tteepe/), and Gerhard Rigoll
+
+## Installation
+
+Only tested with Python 3.8, CUDA 11.8, GCC >= 9.4.0 on NVIDIA RTX 3090, PyTorch 2.0.1 on Ubuntu 22.04.
+
+```bash
+# Setup with miniconda
+conda create -n stmc python=3.8
+conda activate stmc
+
+# Setup torch
+conda install pytorch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2 pytorch-cuda=11.8 -c pytorch -c nvidia
+
+# Setup RAMA
+# (cf. https://github.com/pawelswoboda/RAMA/)
+git clone [email protected]:pawelswoboda/RAMA.git
+mkdir -p RAMA/build && cd RAMA/build
+cmake ..
+make -j 4
+
+# Setup Python bindings
+python -m pip install git+https://github.com/pawelswoboda/RAMA.git
+
+# Install remaining dependencies
+python -m pip install -r requirements.txt
+```
+
+## Data Setup
+
+The config files assume the datasets are stored in `./data/`. You can setup a symlink to a different location or adjust the paths in the config. The datasets are available at:
+
+* [CityFlow](https://www.aicitychallenge.org)
+* [Synthehicle](https://github.com/fubel/synthehicle)
+
+## Running the Code
+
+For a multi-camera scene, adjust the config.yaml and run
+
+```bash
+# for Synthehicle, Town06-O-dawn
+python main.py +experiment=Synthehicle dataset.scene_path=./test/Town06-O-dawn/
+```
+
+❗️ We'll provide all pre-extracted detections and features for CityFlow soon.
+
+## Features and Detections
+
+Our resources are formatted in the MOT-Challenge format, with the addition that the last N columns of a resource file store the appearance feature vector of that object. They will be loaded automatically via hugging face.
+
+## Evaluation
+
+The results are saved in the output directory specified in the config. 
+
+**🚨 Please use the evaluation scripts provided by the respective datasets to evaluate the final results!**
+
+Our in-built evaluation follows the evaluation protocol of Synthehicle which differs from the CityFlow official evaluation script (our eval does not filter single-cam trajectories, for instance). 
+
+## Acknowledgements
+
+We'd like to thank the authors of the following repositories for providing code used in our work:
+
+* We use the [RAMA](git+https://github.com/pawelswoboda/RAMA.git) solver which enables fast multi-cuts on the GPU.
+* The features for CityFlow are from [LCFractal](https://github.com/LCFractal/AIC21-MTMC).
+
+## Citation