README.md

Jaad4MotionBERT

This repository contains code and annotations to create a dataset of individual pedestrian bounding boxes (without the frames) with their 2d keypoints created by OpenPifPaf, and their intention (crossing / not crossing) from the JAAD dataset. The created database is used to train a modified version of the MotionBERT model to predict pedestrian intention in the context of Autonomous Vehicules.

Requirements

The videos need to be imported. This is done by executing the following line in the JAAD_DS folder

./download_clips.sh

Packages required :

conda create -n jaad4motionbert python=3.7 anaconda
conda activate jaad4motionbert
conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
pip install -r requirements.txt

This code was developped and tested with Python 3.7.

Dataset Creation

To create the dataset, open a terminal in the JAAD folder and run the following command :

python3 dataset.py --data_path=<folder_path> --compute_kps --regen

• --data_path helps to specify the folder path if different from the current one
• --compute_kps flag to compute keypoints with bounding boxes. Only the boundingbox will be included in the output pickle file if the flag is omitted.
• --regen flag to regenerate the database

Output

The processed directory tree should look like this:

.
├── README.md
├── dataset.py
└── JAAD_DS
    ├── annotations
    │   └── video_X.xml
    ├── download_clips.sh
    ├── JAAD_clips
    │   └── video_X.mp4
    ├── jaad_dataset.pkl
    └── LICENSE

The output of the code is a pickle file jaad_database.pkl containing a dictionary with the following structure :

'annotations': {
    'vid_id'(str): {
        'num_frames':   int
        'width':        int
        'height':       int
        'ped_annotations'(str): {
            'ped_id'(str): list (dict) {
                'old_id':       str
                'frames':       list (int)
                'occlusion':    list (int)
                'bbox':         list ([x1 (float), y1 (float), x2 (float), y2 (float)])
                '2dkp':         list (array(array))
                'cross':        list (int)}}}}
'split': {
    'train_ID': list (str)
    'test_ID':  list (str)}
'ckpt': str
'seq_per_vid': list (int)