This repository contains new annotations for the Joint Attention in Autonomous Driving (JAAD) dataset. The annotations are in XML format and can be used with a newly introduced python interface. The original annotations can be found here.
Download video clips: YorkU server or Dropbox
JAAD annotations are organized according to video clip names. There are three types of labels, pedestrians (samples with behavior annnotations), peds (bystanders that are far away and do not interact with the driver) and people (groups of pedestrians). Each pedestrian has a unique id in the form of 0_<video_id>_< pedestrian_number>. Pedestrians with behavior annotations have a letter 'b' at the end of their id, e.g. 0_1_3b. The annotations for people also follow the same pattern with the exception of ending with letter 'p', e.g. 0_5_2p.
All samples are annotated with bounding boxes using two-point coordinates (top-left, bottom-right) [x1, y1, x2, y2]. The bounding boxes have corresponding occlusion tags. The occlusion values are either 0 (no occlusion), 1 (partial occlusion >25%) or 2 (full occlusion >75%).
According to their types, the annotations are divided into 5 groups:
Annotations: These include video attributes (time of day, weather, location), pedestrian bounding box coordinates, occlusion information and activities (e.g. walking, looking). The activities are provided only for a subset of pedestrians. These annotations are one per frame per label.
Attributes (pedestrians with behavior annotations only): These include information regarding pedestrians' demographics, crossing point, crossing characteristics, etc. These annotations are one per pedestrian.
Appearance (videos with high visibility only): These include information regarding pedestrian appearance such as pose, clothing, objects carreid (see _get_ped_appearance() for more details). These annotations are one per frame per pedestrian.
Traffic: These provide information about traffic, e.g. signs, traffic light, for each frame. These annotations are one per frame.
Vehicle: These are vehicle actions, e.g. moving fast, speeding up, per each frame.
JAAD contains 346 video clips. These clips should be downloaded and placed in JAAD_clips folder as follows:
JAAD_clips/video_0001.mp4
JAAD_clips/video_0002.mp4
...
To download the videos, either run script download_clips.sh or manually download the clips from here or here and extract the zip archive.
The interface is written and tested using python 3.5. The interface also requires
the following external libraries:
- opencv-python
- numpy
- scikit-learn
In order to use the data, first, the video clips should be converted into images. This can be done using script split_clips_to_frames.sh or via interface as follows:
from jaad_data import JAAD
jaad_path = <path_to_the_dataset_root_folder>
imdb = JAAD(data_path=jaad_path)
imdb.extract_and_save_images()
Using either of the methods will create a folder called images and save the extracted images grouped by corresponding video ids in the folder.
images/video_0001/
00000.png
00001.png
...
images/video_0002/
00000.png
00001.png
...
...
Upon using any methods to extract data, the interface first generates a database (by calling generate_database()) of all annotations in the form of a dictionary and saves it as a .pkl file in the cache directory (the default path is JAAD/data_cache). For more details regarding the structure of the database dictionary see comments in the jaad_data.py for function generate_database().
The interface has the following configuration parameters:
data_opts = {'fstride': 1,
'sample_type': 'all',
'subset': 'high_visibility',
'data_split_type': 'default',
'seq_type': 'trajectory',
'height_rng': [0, float('inf')],
'squarify_ratio': 0,
'min_track_size': 0,
'random_params': {'ratios': None,
'val_data': True,
'regen_data': True},
'kfold_params': {'num_folds': 5, 'fold': 1}}
'fstride'. This is used for sequence data. The stride specifies the sampling resolution, i.e. every nth frame is used
for processing.
'sample_type'. This method specifies whether to extract all the pedestrians or only the ones with behavior data (beh).
'subset'. Specifies which subset of videos to use based on degree of visibility and resolution.
'data_split_type'. The JAAD data can be split into train/test or val in three different ways. default uses the predefined train/val/test split specified in .txt files in split_ids folder. random randomly divides pedestrian ids into train/test (or val) subsets depending on random_params (see method _get_random_pedestrian_ids() for more information). kfold divides the data into k sets for cross-validation depending on kfold_params (see method _get_kfold_pedestrian_ids() for more information).
'seq_type'. Type of sequence data to generate (see Sequence analysis).
'height_rng'. These parameters specify the range of pedestrian scales (in pixels) to be used. For example height_rng': [10, 50] only uses pedestrians within the range of 10 to 50 pixels in height.
'squarify_ratio'. This parameter can be used to fix the aspect ratio (width/height) of bounding boxes. 0 the original bounding boxes are returned.
'min_track_size'. The minimum allowable sequence length in frames. Shorter sequences will not be used.
There are three built-in sequence data generators accessed via generate_data_trajectory_sequence(). The type of sequences generated are trajectory, intention and crossing. To create a custom data generator, follow a similar structure and add a function call to generate_data_trajectory_sequence() in the interface.
The interface has a method called get_detection_data() which can be used to generate detection data. Currently, there are four build-in methods specified which either return data or produce and save data lists for models (see get_detection_data() for more information).
If you use our dataset, please cite:
@inproceedings{rasouli2017they,
title={Are They Going to Cross? A Benchmark Dataset and Baseline for Pedestrian Crosswalk Behavior},
author={Rasouli, Amir and Kotseruba, Iuliia and Tsotsos, John K},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
pages={206--213},
year={2017}
}
@inproceedings{rasouli2018role,
title={It’s Not All About Size: On the Role of Data Properties in Pedestrian Detection},
author={Rasouli, Amir and Kotseruba, Iuliia and Tsotsos, John K},
booktitle={ECCVW},
year={2018}
}
Please send email to [email protected] or [email protected] if there are any problems with downloading or using the data.
This project is licensed under the MIT License - see the LICENSE file for details.
The video clips are licensed under Creative Commons Attribution 4.0 International License.