ITOP Dataset

Haque, Albert; Peng, Boya; Luo, Zelun; Alahi, Alexandre; Yeung, Serena; Fei-Fei, Li
doi:10.5281/zenodo.3932973
October 8, 2016 Dataset Open Access
ITOP Dataset

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

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    <dct:description>&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;</dct:description>
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1,592
6,614
views
downloads
See more details...
	All versions	This version
Views	1,592	1,592
Downloads	6,614	6,614
Data volume	18.8 TB	18.8 TB
Unique views	1,320	1,320
Unique downloads	1,012	1,012
More info on how stats are collected.
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Publication date:
October 8, 2016
DOI:
Keyword(s):
depth sensor human pose estimation computer vision 3D vision
Related identifiers:
Cites
arXiv:1603.07076 (Preprint)
License (for files):
Creative Commons Attribution 4.0 International
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Version 1.0 10.5281/zenodo.3932973
Oct 8, 2016
Cite all versions? You can cite all versions by using the DOI 10.5281/zenodo.3932972. This DOI represents all versions, and will always resolve to the latest one. Read more.
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