Dataset Open Access

ITOP Dataset

Haque, Albert; Peng, Boya; Luo, Zelun; Alahi, Alexandre; Yeung, Serena; Fei-Fei, Li

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  <identifier identifierType="DOI">10.5281/zenodo.3932973</identifier>
  <creators>
    <creator>
      <creatorName>Haque, Albert</creatorName>
      <givenName>Albert</givenName>
      <familyName>Haque</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0001-6769-6370</nameIdentifier>
      <affiliation>Stanford University</affiliation>
    </creator>
    <creator>
      <creatorName>Peng, Boya</creatorName>
      <givenName>Boya</givenName>
      <familyName>Peng</familyName>
      <affiliation>Stanford University</affiliation>
    </creator>
    <creator>
      <creatorName>Luo, Zelun</creatorName>
      <givenName>Zelun</givenName>
      <familyName>Luo</familyName>
      <affiliation>Stanford University</affiliation>
    </creator>
    <creator>
      <creatorName>Alahi, Alexandre</creatorName>
      <givenName>Alexandre</givenName>
      <familyName>Alahi</familyName>
      <affiliation>Stanford University</affiliation>
    </creator>
    <creator>
      <creatorName>Yeung, Serena</creatorName>
      <givenName>Serena</givenName>
      <familyName>Yeung</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-0529-0628</nameIdentifier>
      <affiliation>Stanford University</affiliation>
    </creator>
    <creator>
      <creatorName>Fei-Fei, Li</creatorName>
      <givenName>Li</givenName>
      <familyName>Fei-Fei</familyName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0002-7481-0810</nameIdentifier>
      <affiliation>Stanford University</affiliation>
    </creator>
  </creators>
  <titles>
    <title>ITOP Dataset</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2016</publicationYear>
  <subjects>
    <subject>depth sensor</subject>
    <subject>human pose estimation</subject>
    <subject>computer vision</subject>
    <subject>3D vision</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2016-10-08</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/3932973</alternateIdentifier>
  </alternateIdentifiers>
  <relatedIdentifiers>
    <relatedIdentifier relatedIdentifierType="arXiv" relationType="Cites" resourceTypeGeneral="Text">arXiv:1603.07076</relatedIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.3932972</relatedIdentifier>
  </relatedIdentifiers>
  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&lt;p&gt;&lt;strong&gt;Summary&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The ITOP dataset (Invariant Top View) contains 100K depth images from side and top views of a person in a scene. For each image, the location of 15 human body parts are labeled with 3-dimensional (x,y,z) coordinates, relative to the sensor&amp;#39;s position. Read the full paper for more context [&lt;a href="https://arxiv.org/pdf/1603.07076.pdf"&gt;pdf&lt;/a&gt;].&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Getting Started&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Download then decompress the h5.gz file.&lt;/p&gt;

&lt;pre&gt;&lt;code class="language-bash"&gt;gunzip ITOP_side_test_depth_map.h5.gz&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;Using Python and &lt;a href="https://www.h5py.org/"&gt;h5py&lt;/a&gt; (&lt;em&gt;pip install h5py&lt;/em&gt; or &lt;em&gt;conda install h5py&lt;/em&gt;), we can load the contents:&lt;/p&gt;

&lt;pre&gt;&lt;code class="language-python"&gt;import h5py
import numpy as np

f = h5py.File('ITOP_side_test_depth_map.h5', 'r')
data, ids = f.get('data'), f.get('id')
data, ids = np.asarray(data), np.asarray(ids)

print(data.shape, ids.shape)
# (10501, 240, 320) (10501,)&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Note:&lt;/strong&gt; For any of the &lt;em&gt;*_images.h5.gz&lt;/em&gt; files, the underlying file is a tar file and not a h5 file. Please rename the file extension from &lt;em&gt;h5.gz&lt;/em&gt; to &lt;em&gt;tar.gz&lt;/em&gt; before opening. The following commands will work:&lt;/p&gt;

&lt;pre&gt;&lt;code class="language-bash"&gt;mv ITOP_side_test_images.h5.gz ITOP_side_test_images.tar.gz
tar xf ITOP_side_test_images.tar.gz&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Metadata&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;File sizes for images, depth maps, point clouds, and labels refer to the uncompressed size.&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;+-------+--------+---------+---------+----------+------------+--------------+---------+
| View  | Split  | Frames  | People  | Images   | Depth Map  | Point Cloud  | Labels  |
+-------+--------+---------+---------+----------+------------+--------------+---------+
| Side  | Train  | 39,795  |     16  | 1.1 GiB  | 5.7 GiB    | 18 GiB       | 2.9 GiB |
| Side  | Test   | 10,501  |      4  | 276 MiB  | 1.6 GiB    | 4.6 GiB      | 771 MiB |
| Top   | Train  | 39,795  |     16  | 974 MiB  | 5.7 GiB    | 18 GiB       | 2.9 GiB |
| Top   | Test   | 10,501  |      4  | 261 MiB  | 1.6 GiB    | 4.6 GiB      | 771 MiB |
+-------+--------+---------+---------+----------+------------+--------------+---------+&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Data Schema&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;Each file contains several HDF5 datasets at the root level. Dimensions, attributes, and data types are listed below. The key refers to the (HDF5) dataset name. Let &lt;span class="math-tex"&gt;\(n\)&lt;/span&gt; denote the number of images.&lt;br&gt;
&lt;br&gt;
&lt;strong&gt;Transformation&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;To convert from point clouds to a&amp;nbsp;&lt;span class="math-tex"&gt;\(240 \times 320\)&lt;/span&gt; image, the following transformations were used. Let&amp;nbsp;&lt;span class="math-tex"&gt;\(x_{\textrm{img}}\)&lt;/span&gt; and&amp;nbsp;&lt;span class="math-tex"&gt;\(y_{\textrm{img}}\)&lt;/span&gt; denote the&amp;nbsp;&lt;span class="math-tex"&gt;\((x,y)\)&lt;/span&gt; coordinate in the image plane. Using the raw point cloud&amp;nbsp;&lt;span class="math-tex"&gt;\((x,y,z)\)&lt;/span&gt; real world coordinates, we compute the depth map as follows:&amp;nbsp;&lt;span class="math-tex"&gt;\(x_{\textrm{img}} = \frac{x}{Cz} + 160\)&lt;/span&gt; and&amp;nbsp;&lt;span class="math-tex"&gt;\(y_{\textrm{img}} = -\frac{y}{Cz} + 120\)&lt;/span&gt; where &lt;span class="math-tex"&gt;\(C\approx 3.50×10^{−3} = 0.0035\)&lt;/span&gt; is the intrinsic camera calibration parameter. This results in the depth map:&amp;nbsp;&lt;span class="math-tex"&gt;\((x_{\textrm{img}}, y_{\textrm{img}}, z)\)&lt;/span&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Joint ID (Index) Mapping&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;&lt;code&gt;joint_id_to_name = {
  0: 'Head',        8: 'Torso',
  1: 'Neck',        9: 'R Hip',
  2: 'R Shoulder',  10: 'L Hip',
  3: 'L Shoulder',  11: 'R Knee',
  4: 'R Elbow',     12: 'L Knee',
  5: 'L Elbow',     13: 'R Foot',
  6: 'R Hand',      14: 'L Foot',
  7: 'L Hand',
}&lt;/code&gt;&lt;/pre&gt;

&lt;p&gt;&lt;strong&gt;Depth Maps&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;&lt;em&gt;Key:&lt;/em&gt; id

	&lt;ul&gt;
		&lt;li&gt;&lt;em&gt;Dimensions:&lt;/em&gt; &lt;span class="math-tex"&gt;\((n,)\)&lt;/span&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Data Type:&lt;/em&gt; uint8&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Description:&lt;/em&gt; Frame identifier in the form XX_YYYYY where XX is the person&amp;#39;s ID number and YYYYY is the frame number.&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;em&gt;Key: &lt;/em&gt;data
	&lt;ul&gt;
		&lt;li&gt;&lt;em&gt;Dimensions: &lt;/em&gt;&lt;span class="math-tex"&gt;\((n,240,320)\)&lt;/span&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Data Type:&lt;/em&gt; float16&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Description:&lt;/em&gt; Depth map (i.e. mesh) corresponding to a single frame. Depth values are in real world meters (m).&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Point Clouds&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;&lt;em&gt;Key:&lt;/em&gt; id

	&lt;ul&gt;
		&lt;li&gt;&lt;em&gt;Dimensions:&lt;/em&gt; &lt;span class="math-tex"&gt;\((n,)\)&lt;/span&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Data Type:&lt;/em&gt; uint8&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Description:&lt;/em&gt; Frame identifier in the form XX_YYYYY where XX is the person&amp;#39;s ID number and YYYYY is the frame number.&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;em&gt;Key: &lt;/em&gt;data
	&lt;ul&gt;
		&lt;li&gt;&lt;em&gt;Dimensions: &lt;/em&gt;&lt;span class="math-tex"&gt;\((n,76800,3)\)&lt;/span&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Data Type: float16&lt;/em&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Description:&lt;/em&gt; Point cloud containing 76,800 points (240x320). Each point is represented by a 3D tuple measured in real world meters (m).&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Labels&lt;/strong&gt;&lt;/p&gt;

&lt;ul&gt;
	&lt;li&gt;&lt;em&gt;Key: &lt;/em&gt;id

	&lt;ul&gt;
		&lt;li&gt;&lt;em&gt;Dimensions: &lt;/em&gt;&lt;span class="math-tex"&gt;\((n,)\)&lt;/span&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Data Type: &lt;/em&gt;uint8&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Description:&lt;/em&gt; Frame identifier in the form XX_YYYYY where XX is the person&amp;#39;s ID number and YYYYY is the frame number.&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;em&gt;Key: &lt;/em&gt;is_valid
	&lt;ul&gt;
		&lt;li&gt;&lt;em&gt;Dimensions: &lt;/em&gt;&lt;span class="math-tex"&gt;\((n,)\)&lt;/span&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Data Type: &lt;/em&gt;uint8&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Description:&lt;/em&gt; Flag corresponding to the result of the human labeling effort. This is a boolean value (represented by an integer) where a one (1) denotes clean, human-approved data. A zero (0) denotes noisy human body part labels. If is_valid is equal to zero, you should not use any of the provided human joint locations for the particular frame.&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;em&gt;Key: &lt;/em&gt;visible_joints
	&lt;ul&gt;
		&lt;li&gt;&lt;em&gt;Dimensions: &lt;/em&gt;&lt;span class="math-tex"&gt;\((n,15)\)&lt;/span&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Data Type: &lt;/em&gt;int16&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Description:&lt;/em&gt; Binary mask indicating if each human joint is visible or occluded. This is denoted by&amp;nbsp;&lt;span class="math-tex"&gt;\(\alpha\)&lt;/span&gt; in the paper. If&amp;nbsp;&lt;span class="math-tex"&gt;\(\alpha_j=1\)&lt;/span&gt; then the&amp;nbsp;&lt;span class="math-tex"&gt;\(j^{th}\)&lt;/span&gt; joint is visible (i.e. not occluded). Otherwise, if&amp;nbsp;&lt;span class="math-tex"&gt;\(\alpha_j = 0\)&lt;/span&gt; then the &lt;span class="math-tex"&gt;\(j^{th}\)&lt;/span&gt; joint is occluded.&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;em&gt;Key: &lt;/em&gt;image_coordinates
	&lt;ul&gt;
		&lt;li&gt;&lt;em&gt;Dimensions: &lt;/em&gt;&lt;span class="math-tex"&gt;\((n,15,2)\)&lt;/span&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Data Type: &lt;/em&gt;int16&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Description:&lt;/em&gt; Two-dimensional&amp;nbsp;&lt;span class="math-tex"&gt;\((x,y)\)&lt;/span&gt; points corresponding to the location of each joint in the depth image or depth map.&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;em&gt;Key: &lt;/em&gt;real_world_coordinates
	&lt;ul&gt;
		&lt;li&gt;&lt;em&gt;Dimensions: &lt;/em&gt;&lt;span class="math-tex"&gt;\((n,15,3)\)&lt;/span&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Data Type: &lt;/em&gt;float16&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Description:&lt;/em&gt; Three-dimensional&amp;nbsp;&lt;span class="math-tex"&gt;\((x,y,z)\)&lt;/span&gt; points corresponding to the location of each joint in real world meters (m).&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
	&lt;li&gt;&lt;em&gt;Key: &lt;/em&gt;segmentation
	&lt;ul&gt;
		&lt;li&gt;&lt;em&gt;Dimensions: &lt;/em&gt;&lt;span class="math-tex"&gt;\((n,240,320)\)&lt;/span&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Data Type: &lt;/em&gt;&lt;em&gt;int8&lt;/em&gt;&lt;/li&gt;
		&lt;li&gt;&lt;em&gt;Description:&lt;/em&gt; Pixel-wise assignment of body part labels. The background class (i.e. no body part) is denoted by &amp;minus;1.&lt;/li&gt;
	&lt;/ul&gt;
	&lt;/li&gt;
&lt;/ul&gt;

&lt;p&gt;&lt;strong&gt;Citation&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;If you would like to cite our work, please use the following.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Haque A, Peng B, Luo Z, Alahi A, Yeung S, Fei-Fei L. (2016). Towards Viewpoint Invariant 3D Human Pose Estimation. European Conference on Computer Vision. Amsterdam, Netherlands. Springer.&lt;/strong&gt;&lt;/p&gt;

&lt;pre&gt;@inproceedings{haque2016viewpoint,
    title={Towards Viewpoint Invariant 3D Human Pose Estimation},
    author={Haque, Albert and Peng, Boya and Luo, Zelun and Alahi, Alexandre and Yeung, Serena and Fei-Fei, Li},
    booktitle = {European Conference on Computer Vision},
    month = {October},
    year = {2016}
}&lt;/pre&gt;

&lt;ul&gt;
&lt;/ul&gt;</description>
  </descriptions>
</resource>
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Publication date:
October 8, 2016
DOI:
10.5281/zenodo.3932973

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Keyword(s):
depth sensor human pose estimation computer vision 3D vision
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