Dataset Open Access

THOR - people tracks

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


Dublin Core Export

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  <dc:creator>Andrey Rudenko</dc:creator>
  <dc:creator>Tomasz Piotr Kucner</dc:creator>
  <dc:creator>Chittaranjan Sriniva Swaminathan SWAMINATHAN</dc:creator>
  <dc:creator>Ravi Teja Chadalavada</dc:creator>
  <dc:creator>Kaj O. Arras</dc:creator>
  <dc:creator>Achim J. Lilienthal</dc:creator>
  <dc:date>2019-09-10</dc:date>
  <dc:description>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:


	mat - Matlab binary file
	TSV - text file
	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


	3D data

	
		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
		
		
		Column names
		
			Frame - frame ID
			Time - frame timestamp
			[marker name] [C] - coordinate of a [marker name] along [C] axis
		
		
	
	
	6D data
	
		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
		
		
		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}
}

 </dc:description>
  <dc:identifier>https://zenodo.org/record/3382145</dc:identifier>
  <dc:identifier>10.5281/zenodo.3382145</dc:identifier>
  <dc:identifier>oai:zenodo.org:3382145</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/732737/</dc:relation>
  <dc:relation>arxiv:arXiv:1909.04403</dc:relation>
  <dc:relation>doi:10.5281/zenodo.3382144</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>Human Robot Interaction</dc:subject>
  <dc:subject>Motion Prediction</dc:subject>
  <dc:subject>Social Robotics</dc:subject>
  <dc:subject>People Tracking</dc:subject>
  <dc:title>THOR - people tracks</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
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