Context Recognition in Multiple Occupants Situations: Detecting the Number of Agents in a Smart Home Environment with Simple Sensors

Context-recognition and activity recognition systems in multi-user environments such as smart homes, usually assume to know the number of occupants in the environment. However, being able to count ...

[1]  Michael Beigl,et al.  Group activity recognition using belief propagation for wearable devices , 2014, SEMWEB.

[2]  Tao Gu,et al.  Ontology based context modeling and reasoning using OWL , 2004, IEEE Annual Conference on Pervasive Computing and Communications Workshops, 2004. Proceedings of the Second.

[3]  Jian Lu,et al.  Mining Emerging Patterns for recognizing activities of multiple users in pervasive computing , 2009, 2009 6th Annual International Mobile and Ubiquitous Systems: Networking & Services, MobiQuitous.

[4]  Philippe Roose,et al.  Ontology and Rules-Based Model to Reason on Useful Contextual Information for Providing Appropriate Services in U-Healthcare Systems , 2014, IDC.

[5]  Masahiro Sasabe,et al.  Tracking Pedestrians across Multiple Microcells Based on Successive Bayesian Estimations , 2014, TheScientificWorldJournal.

[6]  Go Hasegawa,et al.  Pedestrian counting with grid-based binary sensors based on Monte Carlo method , 2014, SpringerPlus.

[7]  Andrew Blake,et al.  "GrabCut" , 2004, ACM Trans. Graph..

[8]  Abdelhamid Bouchachia,et al.  Multi-resident Activity Recognition Using Incremental Decision Trees , 2014, ICAIS.

[9]  Eun Jung Ko,et al.  Ontology-Based Context Modeling and Reasoning for U-HealthCare , 2007, IEICE Trans. Inf. Syst..

[10]  Diane J. Cook,et al.  Recognizing independent and joint activities among multiple residents in smart environments , 2010, J. Ambient Intell. Humaniz. Comput..

[11]  Mel Siegel,et al.  Sensor data fusion for context-aware computing using dempster-shafer theory , 2004 .

[12]  T. Teixeira,et al.  A Survey of Human-Sensing : Methods for Detecting Presence , Count , Location , Track , and Identity , 2010 .

[13]  Joaquín Salas,et al.  Counting pedestrians with a zenithal arrangement of depth cameras , 2015, Machine Vision and Applications.

[14]  Harry Chen,et al.  SOUPA: standard ontology for ubiquitous and pervasive applications , 2004, The First Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2004. MOBIQUITOUS 2004..

[15]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

[16]  Yi-Ting Chiang,et al.  Strategies for Inference Mechanism of Conditional Random Fields for Multiple-Resident Activity Recognition in a Smart Home , 2010, IEA/AIE.

[17]  Özlem Durmaz Incel,et al.  ARAS human activity datasets in multiple homes with multiple residents , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

[18]  Christian Micheloni,et al.  Video security for ambient intelligence , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[19]  Jian Lu,et al.  Recognizing multi-user activities using wearable sensors in a smart home , 2011, Pervasive Mob. Comput..

[20]  Rong Chen,et al.  A Two-stage Method for Solving Multi-resident Activity Recognition in Smart Environments , 2014, Entropy.

[21]  Mohammed Feham,et al.  Multioccupant Activity Recognition in Pervasive Smart Home Environments , 2015, ACM Comput. Surv..

[22]  Neri Merhav,et al.  Hidden Markov processes , 2002, IEEE Trans. Inf. Theory.

[23]  Lisa M. Brown,et al.  IBM smart surveillance system (S3): a open and extensible framework for event based surveillance , 2005, IEEE Conference on Advanced Video and Signal Based Surveillance, 2005..

[24]  Jr. G. Forney,et al.  The viterbi algorithm , 1973 .

[25]  Thomas B. Moeslund,et al.  Counting the Crowd at a Carnival , 2014, ISVC.