BIWI RGBD-ID Dataset
- Dataset
- Domenica, 21 Luglio 2013 17:27
- Matteo Munaro
- 21096
The BIWI RGBD-ID Dataset is a RGB-D dataset of people targeted to long-term people re-identification from RGB-D cameras.
It contains 50 training and 56 testing sequences of 50 different people. The dataset includes synchronized RGB images (captured at the highest resolution possible with a Microsoft Kinect for Windows, i.e. 1280x960 pixels), depth images, persons' segmentation maps and skeletal data (as provided by Microsoft Kinect SDK), in addition to the ground plane coordinates. These videos have been acquired at about 10fps.
In the training videos, people performs a certain routine of motions in front of a Kinect, such as a rotation around the vertical axis, several head movements and two walks towards the camera.
28 people out of 50 present in the training set have been recorded also in two testing videos each. These testing sequences have been collected in a different day and in a different location with respect to the training dataset, therefore most subjects are dressed differently. For every person in the testing set, a Still sequence and a Walking sequence have been collected. In the Still video, every person is still or slightly moving in place, while in the Walking video, every person performs two walks frontally and two other walks diagonally with respect to the Kinect.
All the samples in the BIWI RGBD-ID Dataset are provided as folders with 5 different files for every frame:
- rgb image (1280x960 resolution)
- depth image (640x480 resolution)
- user map (640x480 resolution)
- txt file with skeleton tracker joint position and links orientation (estimated with Microsoft Kinect SDK)
- txt file with ground plane coefficients
In the figure below, we report samples of rgb, depth, skeleton and user map data for five people of the training set:
Here below, samples from the testing sequences of the same people are shown:
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Copyright (c) 2013 Matteo Munaro.