Modeling Human Activity From Voxel Person Using Fuzzy Logic

As part of an interdisciplinary collaboration on elder-care monitoring, a sensor suite for the home has been augmented with video cameras. Multiple cameras are used to view the same environment and the world is quantized into nonoverlapping volume elements (voxels). Through the use of silhouettes, a privacy protected image representation of the human acquired from multiple cameras, a 3-D representation of the human is built in real time, called voxel person. Features are extracted from voxel person and fuzzy logic is used to reason about the membership degree of a predetermined number of states at each frame. Fuzzy logic enables human activity, which is inherently fuzzy and case-based, to be reliably modeled. Membership values provide the foundation for rejecting unknown activities, something that nearly all current approaches are insufficient in doing. We discuss temporal fuzzy confidence curves for the common elderly abnormal activity of falling. The automated system is also compared to a ground truth acquired by a human. The proposed soft computing activity analysis framework is extremely flexible. Rules can be modified, added, or removed, allowing per-resident customization based on knowledge about their cognitive and functionality ability. To the best of our knowledge, this is a new application of fuzzy logic in a novel approach to modeling and monitoring human activity, in particular, the well-being of an elderly resident, from video.

[1]  Trevor P. Martin,et al.  Fuzzy Ambient Intelligence for Next Generation Telecare , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[2]  Zhihai He,et al.  Facilitating interdisciplinary design specification of "smart" homes for aging in place , 2006, MIE.

[3]  Takeo Kanade,et al.  A real time system for robust 3D voxel reconstruction of human motions , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[4]  Naoya Ohta,et al.  A statistical approach to background subtraction for surveillance systems , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[5]  Paul D. Gader,et al.  Generalized hidden Markov models. I. Theoretical frameworks , 2000, IEEE Trans. Fuzzy Syst..

[6]  A. Nusimow.  Intelligent Video for Homeland Security Applications , 2007, 2007 IEEE Conference on Technologies for Homeland Security.

[7]  Wray L. Buntine.  Operations for Learning with Graphical Models , 1994, J. Artif. Intell. Res..

[8]  Lotfi A. Zadeh,et al.  Outline of a New Approach to the Analysis of Complex Systems and Decision Processes , 1973, IEEE Trans. Syst. Man Cybern..

[9]  Alex Pentland,et al.  A Bayesian Computer Vision System for Modeling Human Interactions , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  A. Laurentini,et al.  The Visual Hull Concept for Silhouette-Based Image Understanding , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  A. J. Sixsmith,et al.  SIMBAD: Smart inactivity monitor using array-based detector , 2002 .

[12]  Azriel Rosenfeld,et al.  Tracking Groups of People , 2000, Comput. Vis. Image Underst..

[13]  Nicolas Thome,et al.  A HHMM-Based Approach for Robust Fall Detection , 2006, 2006 9th International Conference on Control, Automation, Robotics and Vision.

[14]  Marjorie Skubic,et al.  Hidden Markov Model Symbol Recognition for Sketch-Based Interfaces , 2004, AAAI Technical Report.

[15]  Ahmed M. Elgammal,et al.  A Framework for Feature Selection for Background Subtraction , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[16]  Randall Davis,et al.  HMM-based efficient sketch recognition , 2005, IUI.

[17]  James M. Keller,et al.  Incorporation of Non-euclidean Distance Metrics into Fuzzy Clustering on Graphics Processing Units , 2007, Analysis and Design of Intelligent Systems using Soft Computing Techniques.

[18]  Matthew Brand,et al.  Discovery and Segmentation of Activities in Video , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Gregory Dudek,et al.  On 3-D surface reconstruction using shape from shadows , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[20]  Derek T. Anderson,et al.  Parallelisation of fuzzy inference on a graphics processor unit using the compute nified device architecture , 2008 .

[21]  M. A. Bush,et al.  Training and search algorithms for an interactive wordspotting system , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[22]  E. H. Mamdani,et al.  An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller , 1999, Int. J. Man Mach. Stud..

[23]  James M. Keller,et al.  Linguistic summarization of video for fall detection using voxel person and fuzzy logic , 2009, Comput. Vis. Image Underst..

[24]  Dar-Shyang Lee,et al.  Effective Gaussian mixture learning for video background subtraction , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[26]  Bruce G. Baumgart,et al.  Geometric modeling for computer vision. , 1974 .

[27]  Nicolas Thome,et al.  Human Body Part Labeling and Tracking Using Graph Matching Theory , 2006, 2006 IEEE International Conference on Video and Signal Based Surveillance.

[28]  W. Eric L. Grimson,et al.  Learning Patterns of Activity Using Real-Time Tracking , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[29]  M. Skubic,et al.  Older adults' attitudes towards and perceptions of ‘smart home’ technologies: a pilot study , 2004, Medical informatics and the Internet in medicine.

[30]  Giovanni Garibotto.  Computer Vision and Pattern Recognition in Homeland Security Applications , 2007, WILF.

[31]  Marjorie Skubic,et al.  Smart Home Sensors for the Elderly: A Model for Participatory Formative Evaluation , 2006 .

[32]  W. P. Zajdel,et al.  Bayesian visual surveillance : from object detection to distributed cameras , 2006 .

[33]  James M. Keller,et al.  Speedup of fuzzy logic through stream processing on Graphics Processing Units , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[34]  Alex Pentland,et al.  Coupled hidden Markov models for complex action recognition , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[35]  James M. Keller,et al.  Speedup of Fuzzy Clustering Through Stream Processing on Graphics Processing Units , 2008, IEEE Transactions on Fuzzy Systems.

[36]  Larry S. Davis,et al.  W4: Real-Time Surveillance of People and Their Activities , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Stuart J. Russell,et al.  Dynamic bayesian networks: representation, inference and learning , 2002 .

[38]  Tieniu Tan,et al.  Silhouette Analysis-Based Gait Recognition for Human Identification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[39]  Zhihai He,et al.  Recognizing Falls from Silhouettes , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[40]  Paul R. Cohen,et al.  Camera Calibration with Distortion Models and Accuracy Evaluation , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[41]  Baba C. Vemuri,et al.  3-D MODEL CONSTRUCTION FROM MULTIPLE VIEWS USING RANGE AND INTENSITY DATA. , 1986 .

[42]  Jeff A. Bilmes,et al.  A gentle tutorial of the em algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models , 1998 .

[43]  Montse Pardàs,et al.  Foreground Regions Extraction and Characterization Towards Real-Time Object Tracking , 2005, MLMI.

[44]  Marjorie Skubic,et al.  TigerPlace, A State-Academic-Private Project to Revolutionize Traditional Long-Term Care , 2008, Journal of housing for the elderly.