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DAFT & RGB-D Texture Feature Evaluation toolkit

These RGBD Datasets can be used to evaluate texture feature detectors & descriptors that use the depth information provided by a Kinect camera.

Parts of it were created for the publication [Distinctive Texture Features from Perspective-Invariant Keypoints] (implementation at ).
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            [abstract] => I started working as a research assistant at the Intelligent Autonomous Systems Group in December 2011 and I am mainly cooperating with Dejan Pangeric, Dipl.-Ing. I am mostly involved in robotics perception, however, I have very broad interests in robotics and machine learning fields. I am currently undertaking Master of Robotics, Cognition and Intelligence at Technical University of Munich.
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I'm a postdoctoral researcher supported from ERASMUS MUNDUS BEAM scholarship of European Commission. I got my Ph.D degree from the department of Mechanical and Aerospace Engineering of Seoul National University (advised by Prof. Frank C. Park) and worked as a postdoctoral researcher for Humanoid Robot Research Center (HUBO Lab) in 2011.
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Before I joined the Intelligent Autonomous Systems Group I studied Computer Science at TUM. I'm currently working on the import of knowledge from the world wide web into the knowledge base of our mobile robots. In particular, my current research focuses on understanding natural-language, in order to enable a robot to autonomously acquire new high-level skills by querying web pages such as eHow.com or wikiHow.com.
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            [abstract] => We are looking for the students to work on the following topics as within their B.Sc., M.Sc, Guided Research projects:

	*  In-hand object of daily use modelling (together with Slobodan Ilic)
	*  Robust and accurate object of daily use tracking for mobile manipulation ([decription], together with Sorin Grigorescu)
	*   Knowledge modeling for robot indoor mapping ([description], supervised by Ziyuan Liu)
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I obtained the Doctor in computer science from TUM in November 2011 for my thesis titled Knowledge Processing for Autonomous Robots. Previously, I studied Electrical Engineering and Information Technology at the RWTH Aachen, Germany, and Robotics and Embedded Systems at the ENSTA in Paris, France. I spent one year as an intern at the Accenture Technology Labs in …
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            [abstract] => Professors

	*  Prof. Dr. Bernd Radig
	*  Prof. Dr. Carsten Steger

Prof. Michael Beetz has moved to the University of Bremen in Summer 2012, and parts of the group will follow over the course of the next months. The website of the IAS group in Bremen can be found at .
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            [abstract] => Currently we have four robot platforms in our lab: PR2, TUM Rosie, iCub and Bender. The PR2 was provided by the Willow Garage Beta Program. TUM Rosie has been built around the KUKA-omnidirectional base and KUKA-lightweight LWR-4 arms. The iCub is a small-size humanoid robot being designed by the RobotCub Consortium, consisting of several European universities. B21 is the alumnus of robots in our robot-park.
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            [abstract] => Vorlesungen

	*  Technical Cognitive Systems (IN2222)
	*  Bildverstehen I: Methoden der industriellen Bildverarbeitung (IN2023)

Seminar

	*  Master-Seminar: Robot Learning (IN2107, IN8901, IN4611)
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	*  Bildverstehen I: Methoden der industriellen Bildverarbeitung (IN2023)
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    [creator] => Prof. Dr. Carsten Steger
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            [abstract] => Lectures

	*  Bildverstehen I: Methoden der industriellen Bildverarbeitung (IN2023)
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    [creator] => Moritz Tenorth
    [user] => tenorth
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            [tenorth] => Moritz Tenorth
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            [abstract] => Seminar

	*  Master-Seminar - Wissensrepräsentation in der künstlichen Intelligenz (IN2107, IN8901, IN4611)
	*  Master-Seminar - Mensch-Roboter Interaktion (IN2107, IN8901, IN4532)

Praktikum

	*  Master-Praktikum - Sensorgestützte intelligente Umgebungen (IN2106, IN8902, IN4053)
	*  Master-Praktikum - KI-basierte Robotersteuerung (IN2106, IN8902, IN4056)
	*  Master-Praktikum - Innovation@CoTeSys (IN2106, IN8902, IN4109)
        )

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            [sturmju] => Juergen Sturm
            [lohr] => Quirin Lohr
            [tenorth] => Moritz Tenorth
            [jain] => Dominik Jain
            [kirsch] => Dr. Alexandra Kirsch
            [weikersd] => David Tim Weikersdorfer
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            [abstract] => Large-scale integrative projects

	*  cotesys: Cognition for Technical Systems

Task-level planning and execution

	*  cram: Cognitive Robot Abstract Machine
	*  para: Planning for Adaptive Robot Assistance
	*  cogito: Plan-based control of robotic agents
        )

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            [tenorth] => Moritz Tenorth
            [jain] => Anand Dominik Jain
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    [title] => Software and data sets
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            [abstract] => Open-source software packages

	*  ROS stacks and packages: Contributions of the IAS Group to the Robot Operating System (ROS)
	*  The ProbCog Toolbox for Statistical Relational Learning and Reasoning: ProbCog designs and implements a software suite for statistical relational learning that is publicly available for download.
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    [creator] => Dejan Pangercic
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            [isikm] => Mehmet Can Isik
            [moesenle] => Lorenz Mösenlechner
            [hausman] => Karol Hausman
            [blodow] => Nico Blodow
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    [title] => Internal IAS Workshop on Knowrob, CRAM and Semantic Perception
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            [abstract] => NOTE: The content is subject to frequent changes, please check back regularly.

Purpose

The purpose of this workshop is to introduce some of the core technologies developed in the IAS group over the last years for knowledge-enabled service robotics to our freshmen, colleagues from Bremen and guests.
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    [creator] => Moritz Tenorth
    [user] => tenorth
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            [id] => events:knowledge-workshop-iros2011
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            [extra] => 
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    [title] => IROS 2011 Workshop: Knowledge Representation for Autonomous Robots
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            [abstract] => Over the past years, computers have made significant progress in mining knowledge from the web, aggregating information from different sources and using this knowledge to answer complex queries. Watson's recent victory over human jeopardy players impressively shows that very complex tasks in terms of knowledge acquisition, question answering, and natural language understanding can be solved nowadays.
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    [creator] => Dejan Pangercic
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    [title] => ICRA 2012 Workshop on Semantic Perception and Mapping for Knowledge-enabled Service Robotics
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            [abstract] => What: 

ICRA 2012 WORKSHOP 

Semantic Perception and Mapping for Knowledge-enabled Service Robotics (with interactive session and demonstrations) 

Where: 

ICRA 2012, St. Paul, Minnesota, USA (Room #9, River Centre) 

When: 

May 18th, 2012

Website: 


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            [abstract] => Summer schools and tutorials

	*  CoTeSys-ROS Fall School on Cognition Enabled Mobile Manipulation (November 2010)
	*  Player Summer School on Cognitive Robotics (August 2007)

Workshops

	*  Internal IAS Workshop on KnowRob, CRAM and Perception  (June 2012)
	*  ICRA 2012: Workshop on Semantic Perception and Mapping for Knowledge-enabled Service Robotics (May 2012)
	*  IROS 2011: Workshop on Knowledge Representation for Autonomous Robots (October 2011)
	*  euRobotics 2011: RGB-D Workshop on 3D P…
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Persönlicher Status und Werkzeuge

Home Software and data sets TUM Kitchen Data Set

TUM Kitchen Data Set

Introduction

The TUM Kitchen Data Set is provided to foster research in the areas of markerless human motion capture, motion segmentation and human activity recognition. It should aid researchers in these fields by providing a comprehensive collection of sensory input data that can be used to try out and to verify their algorithms. It is also meant to serve as a benchmark for comparative studies given the manually annotated “ground truth” labels of the underlying actions. The recorded activities have been selected with the intention to provide realistic and seemingly natural motions, and consist of everyday manipulation activities in a natural kitchen environment.


Description of the Data

The TUM Kitchen Data Set contains observations of several subjects setting a table in different ways. Some perform the activity like a robot would do, transporting the items one-by-one, other subjects behave more natural and grasp as many objects as they can at once. In addition, there are two episodes where the subjects repetitively performed reaching and grasping actions. Applications of the data are mainly in the areas of human motion tracking, motion segmentation, and activity recognition.

To provide sufficient information for recognizing and characterizing the observed activities, we recorded the following multi-modal sensor data:

  • Video data from four fixed, overhead cameras (384×288 pixels RGB color or 780×582 pixels raw Bayer pattern, at 25Hz)
  • Motion capture data (*.bvh file format) extracted from the videos using our markerless full-body MeMoMan tracker
  • RFID tag readings from three fixed readers embedded in the environment (sample rate 2Hz)
  • Magnetic (reed) sensors detecting when a door or drawer is opened. (sample rate 10Hz)
  • Action labels (the data is labeled separately for the left hand, the right hand, and the trunk of the person)

A more detailed documentation of the data set is provided in the following Technical Report: The TUM Kitchen Data Set

Publications

Workshop Papers

The TUM Kitchen Data Set of Everyday Manipulation Activities for Motion Tracking and Action Recognition (Moritz Tenorth, Jan Bandouch, Michael Beetz), In IEEE International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences (THEMIS), in conjunction with ICCV2009, 2009. [bib] [pdf]
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News

  • January 21, 2012: The mesh models describing the surface of the different subjects have been uploaded. For a description, see below.
  • March 16, 2010: Angela Yao, ETH Zürich, contributed labels for almost all remaining sequences. Thanks a lot!
  • February 8, 2010: Both the BVH files and the extrinsic calibration have been adapted to match the coordinate system of the CSV files (previously, there was an erroneous rotation that resulted in inverted values for y and z).
  • February 8, 2010: Added MATLAB calibration provided by Jürgen Gall, BIWI, ETH Zürich.
  • February 5, 2010: The JPG sequences now all start at frame 0, allowing for using the first part for calculating the background subtraction.
  • October 27, 2009: Fixed a small issue regarding the frame time of the BVH files, which was given too low before. The corrected files are available below.
  • September 18, 2009: Eight new sequences have been added to the data set, and some problems with different coordinate systems are resolved.
  • July 28, 2009: The paper “The TUM Kitchen Data Set of Everyday Manipulation Activities for Motion Tracking and Action Recognition” desribing the data set has been accepted for publication during the THEMIS workshop in conjunction with ICCV 2009.
  • May 18, 2009: Data set released to the public. If you encounter problems or have ideas what to improve, please contact Moritz Tenorth or Jan Bandouch. We will add more data and documentation in the future.

Episodes

ID Subject task video mocap sensor data startframe endframe labels
0-0 S01 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 240 1480 csv
0-1 S01 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 255 1530 csv
0-2 S01 stt human avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 234 1190 csv
0-3 S02 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 306 2206 csv
0-4 S02 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 256 1763 csv
0-6 S02 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 281 1645 csv*
0-7 S01 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 240 1720 csv*
0-8 S01 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 237 1520 csv*
0-9 S04 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 254 1910 csv*
0-10 S04 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 215 1850 csv*
0-11 S03 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 290 2100 csv*
0-12 S03 stt human avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 265 1400 csv*
1-0 S01 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 309 2320 csv
1-1 S01 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 195 2030 csv
1-2 S01 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 228 2000 csv
1-3 S01 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 255 1988 csv
1-4 S01 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 242 1890 csv
1-5 S01 repetitive avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 205 6710 csv
1-6 S03 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 239 2280 csv
1-7 S03 stt robot avi raw jpg avi raw jpg avi raw jpg avi raw jpg bvh csv rfid doors 200 2060 csv*

* Labels provided by Angela Yao, ETH Zürich

Tracking starts at the video frame startframe and finishes at endframe, i.e. the first row in the pose files of episode 0-0 corresponds to the video frame 240 in that episode.

The task descriptions read as follows:

  • stt: set the table
  • robot: transport each object separately, as done by an inefficient robot
  • human: take several objects at once, as humans usually do
  • repetitive: iteratively picking up and putting down objects from and to different places

The video data is available in three different formats:

  • avi: xvid encoded RGB color video of size 384×288 pixels
  • raw: mpeg4 (lavc) encoded video of original raw camera input stream (monochrome Bayer pattern RGGB) of size 780×582 pixels
  • jpg: gzipped tar archive of jpeg files for each frame (size 384×288 pixels) ←- huge file size!

Additional Information and Tools

Last edited 21.01.2013 09:02 by tenorth