Dataset Open Access

THOR - people tracks

Andrey Rudenko; Tomasz Piotr Kucner; Chittaranjan Sriniva Swaminathan SWAMINATHAN; Ravi Teja Chadalavada; Kaj O. Arras; Achim J. Lilienthal

THÖR is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies.

The dataset includes 13 separate recordings in 3 variations:

  • ``One obstacle" - features one obstacle in the environment and no robot
  • ``Moving robot" - features one obstacle in the environment and the moving robot
  • ``Three obstacles" - features three obstacles in the environment and no robot

THOR - people tracks is the part of THÖR data set containing ground truth position of people in the environment, including information about head orientation.  The data are available in three formats:

  1. mat - Matlab binary file
  2. TSV - text file
  3. bag - ROS bag file

MAT files

  • File - [char] Path to original QTM file
  • Timestamp - [string] Date and time of the startof the data collection
  • Start Fram - [char] 1
  • Frames - [double] Number of frames in the file
  • FrameRate - [double] Number of frames per second
  • Events - [struct] 0
  • Trajectories - [struct] 3D postion of observed reflective markers
    • Labeled  - [struct] Markers belonging to the tracked agents:
      • Count - [double] Number of tracked markers
      • Labels - [cell] List of marker labels
      • Data - [double] Array of dimension {Count}x4x{Frames}, contains the 3D position of each marker and residue
  • RigidBodies - [struct] 6D pose of the helmet, corresponds to head poistion and orientation:
    • Bodies - [double] Number of tracked bodies
    • Name  - [cell] Bodies Names
    • Positions - [double] Array of dimension {Bodies}x3x{Frames} contains the position of the centre of the mass of the markers defining the rigid body
    • Rotations - [double] Array of dimension {Bodies}x9x{Frames} contains rotation matrix describing the orientation of the rigid body
    • RPYs  - [double] Array of dimension {Bodies}x3x{Frames} contains orientation of the rigid body described as RPY angles
    • Residual - [double] Array of dimension {Bodies}x1x{Frames} contains residual for each rigid body

TSV files

  1. 3D data
    1. File Header
      • NO_OF_FRAMES  - number of frames in the file  
      • NO_OF_CAMERAS - number of cameras tracking makers
      • NO_OF_MARKERS - number of tracked markers
      • FREQUENCY - tracking frequency [Hz]   
      • NO_OF_ANALOG - number of analog inputs   
      • ANALOG_FREQUENCY - frequency of analog input   
      • DESCRIPTION -  --
      • TIME_STAMP - the beginning of the data recording
      • DATA_INCLUDED - the type of data included
      • MARKER_NAMES - names of tracked makers
    2. Column names
      • Frame - frame ID
      • Time - frame timestamp
      • [marker name] [C] - coordinate of a [marker name] along [C] axis
  2. 6D data
    1. File Header
      • NO_OF_FRAMES  - number of frames in the file  
      • NO_OF_CAMERAS - number of cameras tracking makers
      • NO_OF_MARKERS - number of tracked markers
      • FREQUENCY - tracking frequency [Hz]   
      • NO_OF_ANALOG - number of analog inputs   
      • ANALOG_FREQUENCY - frequency of analog input   
      • DESCRIPTION -  --
      • TIME_STAMP - the beginning of the data recording
      • DATA_INCLUDED - the type of data included
      • BODY_NAMES - names of tracked rigid bodies
    2. Colum Names
      • Frame - frame ID
      • Time - frame timestamp
      • The columns are grouped according to the rigid body. Each group starts with the name of the rigid body and then is followed by the position of the centre of the mas and the orientation expressed as RPY angles and rotation matrix

Reference:

For more details check project website thor.oru.se or check our publications:

@article{thorDataset2019,
title={TH\"OR: Human-Robot Indoor Navigation Experiment
and Accurate Motion Trajectories Dataset},
author={Andrey Rudenko and Tomasz P. Kucner and
Chittaranjan S. Swaminathan and Ravi T. Chadalavada
and Kai O. Arras and Achim J. Lilienthal},
journal={arXiv preprint arXiv:1909.04403},
year={2019}
}

 

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ex1_run1_qualisys.bag
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ex1_run2_qualisys.bag
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ex1_run3_qualisys.bag
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ex1_run4_qualisys.bag
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ex2_run5_qualisys.bag
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ex3_run1_qualisys.bag
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ex3_run2_qualisys.bag
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ex3_run3_qualisys.bag
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ex3_run4_qualisys.bag
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Exp_1_run_1.mat
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Exp_1_run_1.tsv
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Exp_1_run_1_6D.tsv
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Exp_1_run_2.mat
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Exp_1_run_2.tsv
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Exp_1_run_2_6D.tsv
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Exp_1_run_3.mat
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Exp_1_run_3.tsv
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Exp_1_run_4.mat
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Exp_1_run_4.tsv
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Exp_2_run_1.mat
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Exp_2_run_1.tsv
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Exp_2_run_2.mat
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Exp_2_run_2.tsv
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Exp_2_run_2_6D.tsv
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Exp_2_run_3.mat
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Exp_2_run_3.tsv
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Exp_2_run_3_6D.tsv
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Exp_2_run_4.mat
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Exp_2_run_4.tsv
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Exp_2_run_4_6D.tsv
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Exp_2_run_5.mat
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Exp_2_run_5.tsv
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Exp_2_run_5_6D.tsv
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Exp_3_run_1.mat
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Exp_3_run_1.tsv
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Exp_3_run_1_6D.tsv
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Exp_3_run_2.mat
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Exp_3_run_2.tsv
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Exp_3_run_2_6D.tsv
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Exp_3_run_3.mat
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Exp_3_run_3.tsv
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