A Multi-agent System for Human Activity Recognition in Smart Environments

Activity recognition is an important component for the ambient assisted living systems, which perform home monitoring and assistance of elderly people or patients with risk factors. The paper presents a prototype system for activity recognition based on a multi-agent architecture. In the system, the context of the person is first detected using a domain ontology. Next, the human position is obtained and together with the context forms a sub-activity. The sequence of successive sub-activities is then assembled in a human activity, which is recognized using a stochastic grammar.

[1]  Jake K. Aggarwal,et al.  Recognition of Composite Human Activities through Context-Free Grammar Based Representation , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[2]  Manuela M. Veloso,et al.  Feature selection in conditional random fields for activity recognition , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Thomas Deselaers,et al.  ClassCut for Unsupervised Class Segmentation , 2010, ECCV.

[4]  Avinash C. Kak,et al.  A novel evidence accumulation framework for robust multi-camera person detection , 2008, 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras.

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

[6]  Samy Bengio,et al.  Semi-supervised adapted HMMs for unusual event detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Junji Yamato,et al.  Recognizing human action in time-sequential images using hidden Markov model , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[8]  Irina Mocanu From Content-Based Image Retrieval by Shape to Image Annotation , 2010 .

[9]  Uwe Hansmann,et al.  Pervasive Computing , 2003 .

[10]  Irina Mocanu,et al.  Genetic Algorithms Viewed as Anticipatory Systems , 2010 .

[11]  Dimitris N. Metaxas,et al.  A Framework for Recognizing the Simultaneous Aspects of American Sign Language , 2001, Comput. Vis. Image Underst..

[12]  Rama Chellappa,et al.  Recognition of Multi-Object Events Using Attribute Grammars , 2006, 2006 International Conference on Image Processing.

[13]  Aaron F. Bobick,et al.  Recognition of Visual Activities and Interactions by Stochastic Parsing , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[15]  Manuela M. Veloso,et al.  Conditional random fields for activity recognition , 2007, AAMAS '07.

[16]  Qiang Ji,et al.  Knowledge Based Activity Recognition with Dynamic Bayesian Network , 2010, ECCV.

[17]  Calin Munteanu,et al.  AmIHomCare: a complex ambient intelligent system for home medical assistance , 2011 .