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MPII Human Pose
Classification
2D Keypoints
Pose Estimation
|Action/Event Detection
|...
License: BSD-2-Clause

Overview

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 we
obtained richer annotations including body part occlusions and 3D torso and head orientations.

Data Format

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.pointperson-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 here

Citation

Please use the following citation when referencing the dataset:

@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}
}

License

BSD-2-Clause

Data Summary
Type
Image,
Amount
--
Size
11.26GB
Provided by
Max Planck Institute for Informatics
The Max Planck Institute for Informatics is devoted to cutting-edge research in informatics with a focus on algorithms and their applications in a broad sense.
| Amount -- | Size 11.26GB
MPII Human Pose
Classification 2D Keypoints
Pose Estimation | Action/Event Detection
License: BSD-2-Clause

Overview

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 we
obtained richer annotations including body part occlusions and 3D torso and head orientations.

Data Format

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.pointperson-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 here

Citation

Please use the following citation when referencing the dataset:

@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}
}

License

BSD-2-Clause

0
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