Dataset Card for MPII Human Pose

MPII Human Pose dataset is a state of the art benchmark for evaluation of articulated human pose estimation.

The dataset includes around 25K images containing over 40K people with annotated body joints.

The images were systematically collected using an established taxonomy of every day human activities.

Overall the dataset covers 410 human activities and each image is provided with an activity label.

Each image was extracted from a YouTube video and provided with preceding and following un-annotated frames.

In addition, for the test set, richer annotations were obtained including body part occlusions and 3D torso and head orientations.

Following the best practices for the performance evaluation benchmarks in the literature we withhold the test annotations to prevent overfitting and tuning on the test set. We are working on an automatic evaluation server and performance analysis tools based on rich test set annotations.

This is a FiftyOne dataset with 24984 samples.

Installation

If you haven't already, install FiftyOne:

pip install -U fiftyone

Usage

import fiftyone as fo
import fiftyone.utils.huggingface as fouh

# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = fouh.load_from_hub("Voxel51/MPII_Human_Pose_Dataset")

# Launch the App
session = fo.launch_app(dataset)

Dataset Details

Dataset Description

MPII Human Pose Dataset, Version 1.0 Copyright 2015 Max Planck Institute for Informatics Licensed under the Simplified BSD License Annotations and the corresponding are freely available for research purposes. Commercial use is not allowed due to the fact that the authors do not have the copyright for the images themselves.

License: bsd-2-clause

Dataset Sources

Website: http://human-pose.mpi-inf.mpg.de/#
Paper: http://human-pose.mpi-inf.mpg.de/contents/andriluka14cvpr.pdf

Uses

At the time when dataset was released (2014), dataset was evaluated on 2 main tasks:

Multi-Person Pose Estimation
Single Person Pose Estimation

Dataset Structure

Name:        MPII Human Pose
Media type:  image
Num samples: 24984
Persistent:  True
Tags:        ['version1', 'MPII Human Pose']
Sample fields:
    id:           fiftyone.core.fields.ObjectIdField
    filepath:     fiftyone.core.fields.StringField
    tags:         fiftyone.core.fields.ListField(fiftyone.core.fields.StringField)
    metadata:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.metadata.ImageMetadata)
    rectangle_id: fiftyone.core.fields.ListField(fiftyone.core.fields.IntField)
    activity:     fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Classifications)
    head_rect:    fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Detections)
    objpos:       fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Keypoints)
    scale:        fiftyone.core.fields.VectorField
    annopoints:   fiftyone.core.fields.EmbeddedDocumentField(fiftyone.core.labels.Keypoints)
    video_id:     fiftyone.core.fields.StringField
    frame_sec:    fiftyone.core.fields.IntField

Dataset Creation

Source Data

Data Collection and Processing

See Section 2. Dataset - Data collection paragraph of this paper

Who are the source data producers?

Source data producers

Annotations

Annotation description

Annotations are stored in a matlab structure RELEASE having following fields

.annolist(imgidx) - annotations for image imgidx
    .image.name - image filename
    .annorect(ridx) - body annotations for a person ridx
        .x1, .y1, .x2, .y2 - coordinates of the head rectangle
        .scale - person scale w.r.t. 200 px height
        .objpos - rough human position in the image
        .annopoints.point - person-centric body joint annotations
            .x, .y - coordinates of a joint
            id - joint id (0 - r ankle, 1 - r knee, 2 - r hip, 3 - l hip, 4 - l knee, 5 - l ankle, 6 - pelvis, 7 - thorax, 8 - upper neck, 9 - head top, 10 - r wrist, 11 - r elbow, 12 - r shoulder, 13 - l shoulder, 14 - l elbow, 15 - l wrist)
            is_visible - joint visibility
    .vidx - video index in video_list
    .frame_sec - image position in video, in seconds

img_train(imgidx) - training/testing image assignment

single_person(imgidx) - contains rectangle id ridx of sufficiently separated individuals

act(imgidx) - activity/category label for image imgidx
    act_name - activity name
    cat_name - category name
    act_id - activity id

video_list(videoidx) - specifies video id as is provided by YouTube. To watch video on youtube go to https://www.youtube.com/watch?v=video_list(videoidx)

Annotation process

See Section 2. Dataset - Data annotation paragraph of this paper

Citation

BibTeX:

@inproceedings{andriluka14cvpr,
               author = {Mykhaylo Andriluka and Leonid Pishchulin and Peter Gehler and Schiele, Bernt}
               title = {2D Human Pose Estimation: New Benchmark and State of the Art Analysis},
               booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
               year = {2014},
               month = {June}
}

More Information

The following Github repo contains code to parse the raw data (in MATLAB format) and convert it into a FiftyOne Dataset

Dataset conversion and data card contributed by Loic Mandine