Low cost activity recognition using depth cameras and context dependent spatial regions

Recognition of human activities is usually based on expensive sensor setups to extract rich information such as body posture or object interaction. We investigate the use of inexpensive depth cameras to perform activity recognition using context dependent spatial regions with two different approaches: Spatio-Temporal Plan Representations and Hierarchical Hidden Markov Models. We evaluate both approaches in a simulated and a real-world environment.

[1]  Li Yujian,et al.  A Normalized Levenshtein Distance Metric , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Jennifer Healey,et al.  A Long-Term Evaluation of Sensing Modalities for Activity Recognition , 2007, UbiComp.

[3]  Moritz Tenorth,et al.  KNOWROB-MAP - knowledge-linked semantic object maps , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.

[4]  Michael Karg,et al.  Acquisition and use of transferable, spatio-temporal plan representations for human-robot interaction , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Wendy A. Rogers,et al.  Older Adults' Acceptance of Assistive Robots for the Home , 2011 .

[6]  Matthai Philipose,et al.  Mining models of human activities from the web , 2004, WWW '04.

[7]  Truyen Tran,et al.  Hierarchical semi-Markov conditional random fields for deep recursive sequential data , 2008, Artif. Intell..

[8]  Nico Blodow,et al.  Autonomous semantic mapping for robots performing everyday manipulation tasks in kitchen environments , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Thomas G. Bever,et al.  Sentence Comprehension: The Integration of Habits and Rules , 2001 .

[10]  Bart Selman,et al.  Unstructured human activity detection from RGBD images , 2011, 2012 IEEE International Conference on Robotics and Automation.

[11]  Wolfram Burgard,et al.  Conceptual spatial representations for indoor mobile robots , 2008, Robotics Auton. Syst..

[12]  Svetha Venkatesh,et al.  Activity recognition and abnormality detection with the switching hidden semi-Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Kent Larson,et al.  Using a Live-In Laboratory for Ubiquitous Computing Research , 2006, Pervasive.

[14]  Michael Karg,et al.  A human morning routine dataset , 2014, AAMAS.

[15]  Svetha Venkatesh,et al.  Recognizing and monitoring high-level behaviors in complex spatial environments , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[16]  Svetha Venkatesh,et al.  Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  Moritz Tenorth,et al.  Towards Automated Models of Activities of Daily Life , 2009 .

[18]  Matthew Klenk,et al.  Representing and Reasoning About Spatial Regions Defined by Context , 2011, AAAI Fall Symposium: Advances in Cognitive Systems.

[19]  Gilberto Echeverria,et al.  Modular open robots simulation engine: MORSE , 2011, 2011 IEEE International Conference on Robotics and Automation.

[20]  Svetha Venkatesh,et al.  Learning Hierarchical Hidden Markov Models with General State Hierarchy , 2004, AAAI.

[21]  Michael Beetz,et al.  Action-related place-based mobile manipulation , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Henry A. Kautz,et al.  Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields , 2007, Int. J. Robotics Res..

[23]  Alan Smaill,et al.  Learning musical pitch structures with hierarchical hidden Markov models , 2005 .

[24]  Moritz Tenorth,et al.  Understanding and executing instructions for everyday manipulation tasks from the World Wide Web , 2010, 2010 IEEE International Conference on Robotics and Automation.

[25]  Yoram Singer,et al.  The Hierarchical Hidden Markov Model: Analysis and Applications , 1998, Machine Learning.

[26]  David Wetherall,et al.  Recognizing daily activities with RFID-based sensors , 2009, UbiComp.

[27]  D Feil-Seifer,et al.  Socially Assistive Robotics , 2011, IEEE Robotics & Automation Magazine.