Gaze behaviors such as eye-contact or shared attention are important markers for diagnosing developmental disorders in children. While previous studies have looked at some of these elements, the analysis is usually performed on private datasets and is restricted to lab settings. Furthermore, all publicly available gaze target prediction benchmarks mostly contain instances of adults, which makes models trained on them less applicable to scenarios with young children. In this paper, we propose the first study for predicting the gaze target of children and interacting adults. To this end, we introduce the ChildPlay Gaze dataset: a curated collection of short video clips featuring children playing and interacting with adults in uncontrolled environments (e.g. kindergarten, therapy centers, preschools etc.), which we annotate with rich gaze information. Our results show that looking at faces prediction performance on children is much worse than on adults, and can be significantly improved by fine-tuning models using child gaze annotations.

Dataset Description

The ChildPlay Gaze dataset is composed of 401 clips extracted from 95 longer YouTube videos, totaling 120549 frames. For each clip, we select up to 3 people, and annotate all of them in each frame (when they are visible) with gaze information.

The annotations folder contains 3 subfolders: train, val and test. Each subfolder contains csv annotation files in the format videoid_startframe_endframe.csv. In this naming convention, videoid refers to the original video from which the clip was extracted, while the startframe and endframe refer to the starting and ending frames of the clip in the original YouTube video videoid.

For example, one of the original videos downloaded from YouTube is 1Ab4vLMMAbY.mp4 where 1Ab4vLMMAbY is the YouTube video ID, which can be used directly to build the URL (i.e. https://www.youtube.com/watch?v=1Ab4vLMMAbY). The annotation file 1Ab4vLMMAbY_2354-2439.csv found under ChildPlay/annotations/test refers to the annotation of the clip 1Ab4vLMMAbY_2354-2439.mp4 extracted from 1Ab4vLMMAbY.mp4 which starts at frame 2354 and ends at frame 2439 (included). The numbering starts from 1.

Please note that some videos were recorded at 60 FPS, whereas most are at 24-30. When we extracted clips from these, we also downsampled to 30 FPS by skipping every other frame. The starting and ending frames in their names correspond to their numbers in the original video, but we also include the mention downsampled so they are recognizable. For example, smwfiZd8HLc_7508-8408-downsampled.mp4 is a clip extracted between frames 7508 and 8408 from the video smwfiZd8HLc.mp4. However, it only contains 451 frames as opposed to the expected 901 = 8408 - 7508 + 1 since it was downsampled.

Each annotation csv file has one row per annotated person per frame, and includes the following columns:

clip: the name of the clip (without extension). This value is duplicated across the entire dataframe.
frame: the relative frame number in the clip. For example, frame n refers to the nth frame of the clip. If the clip is named videoid_start-end, then frame n in the clip corresponds to frame start + n - 1 in the original video (unless the clip was downsampled).
person_id: an id used to separate and track annotated people in the clip.
bbox_x: the x-value of the upper left corner of the head bounding box of the person (in the image frame).
bbox_y: the y-value of the upper left corner of the head bounding box of the person (in the image frame).
bbox_width: the width of the head bounding box of the person (in the image frame).
bbox_height: the height of the head bounding box of the person (in the image frame).
gaze_class: a gaze label to determine the type of gaze behavior. One of [inside_visible, outside_frame, gaze_shift, inside_occluded, inside_uncertain, eyes_closed]. Refer to the paper for the definitions of these flags. The labels inside_visible and outside_frame in particular, correspond to the standard inside vs outside label found in other gaze following datasets (e.g. GazeFollow and VideoAttentionTarget)
gaze_x: the x-value of the target gaze point (in the image frame). This value is set to -1 when gaze_class != inside_visible.
gaze_y: the y-value of the target gaze point (in the image frame). This value is set to -1 when gaze_class != inside_visible.
is_child: a binary flag denoting whether the person is a child or an adult.

You will also find a videos.csv file containing a list of videos to download, from which the ChildPlay clips were extracted, along with other metadata (e.g. channel ID, fps, resolution, etc.). There is also a clips.csv file containing similar information but for each clip, and a splits.csv detailing the train/val/test split of each clip.

Furthermore, we provide utility scripts to extract the necessary clips and images from the videos (assuming you have already downloaded them).

Dataset Acquisition

Please follow the steps below to setup the dataset:

Download the 95 original videos listed in videos.csv from YouTube. You can use the python package pytube or some other tool.
Use the extract-clips-from-videos.py script to extract both the clips and corresponding frames from the videos. In order to use the script, you have to supply the following flags --clip_csv_path (path to the clips.csv file), --video_folder (path to the folder of the downloaded videos), --clip_folder (path where to save the clips, it will be created if it doesn't exist), --image_folder (path where to save the images, it will be created if it doesn't exist). Please note that you need to have the packages pandas, tqdm and opencv installed. The script also requires ffmpeg for the extraction.

The final dataset folder structure should look like

.
├── annotations
│   ├── test
│   │   ├── 1Ab4vLMMAbY_2354-2439.csv
│   │   ├── ...
│   ├── train
│   │   ├── 1Aea8BH-PCs_1256-1506.csv
│   │   ├── ...
│   ├── val
│   │   ├── bI1GohGXSt0_2073-2675.csv
│   │   ├── ...
├── clips
│   ├── 1Ab4vLMMAbY_2354-2439.mp4
├── images
│   ├── 1Ab4vLMMAbY_2354-2439
│   │   ├── 1Ab4vLMMAbY_2354.jpg
│   │   ├── ...
│   ├── ...
├── videos
│   ├── 1Ab4vLMMAbY.mp4
│   ├── ...
├── clips.csv
├── README.md
├── extract-clips-from-videos.py
├── splits.csv
└── videos.csv

Contact

Please reach out to Samy Tafasca (samy.tafasca@idiap.ch) or Jean-Marc Odobez (odobez@idiap.ch) if you have any questions, or if some videos are no longer available on YouTube.

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