Retracking-by-Prediction

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Retracking-by-Prediction

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Code and data for "Towards Robust Human Trajectory Prediction in Raw Videos" IROS 2021

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# Towards Robust Human Trajectory Prediction in Raw Videos
The repository contains the code and data for "[Towards Robust Human Trajectory Prediction in Raw Videos](https://arxiv.org/abs/2108.08259)" IROS 2021.

## Dataset

### SDD
   Download the [Stanford Drone Dataset (SDD)](https://cvgl.stanford.edu/projects/uav_data/). The ~69G zip file includes the raw videos and annotations.

- **Our refined annotations of SDD** 📢

- We found many outlier bounding boxes in the original annotations via visualization. To better evaluate the tracking, we refined the annotations by removing the outliers. 
    - Our refined annotation files have been uploaded to [Google Drive](https://drive.google.com/drive/folders/1_hMSdAr31l5XoZj3SmW_9QShd_p99yGW?usp=sharing).

### WILDTRACK
   Download the [WILDTRACK Seven-Camera HD Dataset](https://www.epfl.ch/labs/cvlab/data/data-wildtrack/).

- **Our extended annotations of WILDTRACK** 📢

- The original dataset annotated the first 400 frames of each of the seven videos at 2 fps. To better facilitate the behavior learning, we manually annotated the first 900 frames (refined 1\~400 and new 401\~900) for each video with the [annotation tool](https://github.com/cvlab-epfl/multicam-gt).
    - The extended annotation files have been uploaded to [Google Drive](https://drive.google.com/drive/folders/1vVXNmbuOCx4qWyNVfTXPH_8b5xDP4YOj?usp=sharing), including the original WILDTRACK format and the [MOT format](https://motchallenge.net/instructions/). 
    - Our annotations can be used for larger-scale evaluation on WILDTRACK for single-view/multi-view pedestrian detection, tracking, and trajectory prediction.

## Installation
The project was developed based on Python 3.7.9 and PyTorch 1.1.0. The environment can be set up via the [environment.yml](environment.yml) file:
```
conda env create -f environment.yml
conda activate retracking
```

## Experiments
As an example, we show how to run the experiments on the SDD dataset.

1. **Train a prediction model**

```bash
    cd retracking-by-prediction
    python train.py
    ```

2. **Detection, Tracking, Prediction, and Re-tracking**
   
    ```bash
    sh run_test.sh
    ```

3. **Evaluation**
    
    We evaluate the tracking performance via [py-motmetrics](https://github.com/cheind/py-motmetrics). Note: we use L2 distance in meter instead of box IoU as the association metric.

## Citation
If you use the code or data in your research, please cite the paper:
```
@inproceedings{Yu2021-Retracking,
  author    = {Rui Yu and Zihan Zhou},
  title     = {Towards Robust Human Trajectory Prediction in Raw Videos},
  booktitle = {{IEEE/RSJ} International Conference on Intelligent Robots and Systems, {IROS}},
  year      = {2021}
}
```

## License
The project is released under the MIT License. The [SORT](https://github.com/abewley/sort) tracking code should follow its own license (GPL-3.0).

Retracking-by-Prediction

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