Multiagent System for Semantic Categorization of Places Mean the Use of Distributed Surveillance Cameras

Surveillance systems are quite common in almost every building. The current dimension of these systems is huge and involves a great deal of hardware and human resources for achieving these objectives. This paper proposes the use of an agent-based architecture for helping in the categorization of the places where these are deployed. Proposal uses a deep learning model for evaluating the images captured by the cameras and then label the zone where the camera is located.

[1]  Juan M. Corchado,et al.  Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems , 2013, Inf. Sci..

[2]  Tiancheng Li,et al.  Online Adapting the Magnitude of Target Birth Intensity in the PHD Filter , 2014, DCAI 2014.

[3]  Juan M. Corchado,et al.  Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond , 2017, Frontiers of Information Technology & Electronic Engineering.

[4]  Juan M. Corchado,et al.  Unsupervised neural method for temperature forecasting , 1999, Artif. Intell. Eng..

[5]  Juan M. Corchado,et al.  Maximum Likelihood Hebbian Learning Based Retrieval Method for CBR Systems , 2003, ICCBR.

[6]  Miguel Cazorla,et al.  Scene classification based on semantic labeling , 2016, Adv. Robotics.

[7]  Javier Bajo,et al.  GerAmi: Improving Healthcare Delivery in Geriatric Residences , 2008, IEEE Intelligent Systems.

[8]  Bolei Zhou,et al.  Places: An Image Database for Deep Scene Understanding , 2016, ArXiv.

[9]  Frederic Wurtz,et al.  MAS architecture for energy management: Developing smart networks with JADE platform , 2013, 2013 IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA).

[10]  Juan M. Corchado,et al.  An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care , 2009, Int. J. Ambient Comput. Intell..

[11]  Juan M. Corchado,et al.  Tracking Concept Drift at Feature Selection Stage in SpamHunting: An Anti-spam Instance-Based Reasoning System , 2006, ECCBR.

[12]  Juan Manuel Corchado Rodríguez,et al.  Analytical model for constructing deliberative agents , 2002 .

[13]  Javier Moreno,et al.  Optimization of the virtual mouse HeadMouse to foster its classroom use by children with physical disabilities , 2013 .

[14]  Luis Fernando Castillo,et al.  Development of CBR-BDI Agents: A Tourist Guide Application , 2004, ECCBR.

[15]  Juan M. Corchado,et al.  Quantifying the Ocean's CO2 Budget with a CoHeL-IBR System , 2004, ECCBR.

[16]  Miguel Cazorla,et al.  LexToMap: lexical-based topological mapping , 2017, Adv. Robotics.

[17]  Juan M. Corchado,et al.  A Survey of Recent Advances in Particle Filters and Remaining Challenges for Multitarget Tracking , 2017, Sensors.

[18]  Juan M. Corchado,et al.  Random finite set-based Bayesian filters using magnitude-adaptive target birth intensity , 2014, 17th International Conference on Information Fusion (FUSION).

[19]  Juan M. Corchado,et al.  Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods , 2017, Knowl. Based Syst..

[20]  Juan M. Corchado,et al.  Algorithm design for parallel implementation of the SMC-PHD filter , 2016, Signal Process..

[21]  Sara Rodríguez,et al.  A Hash Based Image Matching Algorithm for Social Networks , 2017, PAAMS.

[22]  Juan M. Corchado,et al.  A particle dyeing approach for track continuity for the SMC-PHD filter , 2014, 17th International Conference on Information Fusion (FUSION).

[23]  José A. Gallud,et al.  Tweacher: New proposal for Online Social Networks Impact in Secondary Education , 2013 .

[24]  José Neves,et al.  Guideline formalization and knowledge representation for clinical decision support , 2013 .

[25]  Juan M. Corchado,et al.  A Reasoning Model for CBR_BDI Agents Using an Adaptable Fuzzy Inference System , 2003, CAEPIA.

[26]  Sara Rodríguez,et al.  Pattern Extraction for the Design of Predictive Models in Industry 4.0 , 2017, PAAMS.

[27]  Juan M. Corchado,et al.  CBR based system for forecasting red tides , 2003, Knowl. Based Syst..

[28]  Juan M. Corchado,et al.  Unsupervised learning for financial forecasting , 1998, Proceedings of the IEEE/IAFE/INFORMS 1998 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.98TH8367).

[29]  Óscar García,et al.  A Serious Game to Reduce Consumption in Smart Buildings , 2017, PAAMS.

[30]  Yee Wen Choon,et al.  Differential Bees Flux Balance Analysis with OptKnock for In Silico Microbial Strains Optimization , 2014, PloS one.

[31]  A. Costa,et al.  Increased performance and better patient attendance in an hospital with the use of smart agendas , 2012, Logic Journal of the IGPL.

[32]  Juan M. Corchado,et al.  FSfRT: Forecasting System for Red Tides , 2004, Applied Intelligence.

[33]  Juan M. Corchado,et al.  A polarity analysis framework for Twitter messages , 2015, Appl. Math. Comput..

[34]  Juan M. Corchado,et al.  A forecasting solution to the oil spill problem based on a hybrid intelligent system , 2010, Inf. Sci..

[35]  Juan M. Corchado,et al.  A comparison of Kernel methods for instantiating case based reasoning systems , 2002, Adv. Eng. Informatics.

[36]  Juan M. Corchado,et al.  Forecasting the probability of finding oil slicks using a CBR system , 2009, Expert Syst. Appl..

[37]  Juan M. Corchado,et al.  Hybrid artificial intelligence methods in oceanographic forecast models , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[38]  Bolei Zhou,et al.  Places: A 10 Million Image Database for Scene Recognition , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[39]  Juan M. Corchado,et al.  geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research , 2009, BMC Bioinformatics.

[40]  S Rodriguez,et al.  People detection and stereoscopic analysis using MAS , 2010, 2010 IEEE 14th International Conference on Intelligent Engineering Systems.

[41]  Juan M. Corchado,et al.  A Comparative Performance Study of Feature Selection Methods for the Anti-spam Filtering Domain , 2006, ICDM.

[42]  Juan Manuel Corchado,et al.  Bladder Carcinoma Data with Clinical Risk Factors and Molecular Markers: A Cluster Analysis , 2015, BioMed research international.

[43]  Dimitrios V. Rovas,et al.  Black-Box Optimization for Buildings and Its Enhancement by Advanced Communication Infrastructure , 2013 .

[44]  J. Bajo,et al.  Hybrid multi-agent architecture as a real-time problem-solving model , 2008, Expert Syst. Appl..

[45]  Juan M. Corchado,et al.  Automating the construction of CBR systems using kernel methods , 2001, Int. J. Intell. Syst..

[46]  Juan M. Corchado,et al.  Intelligent business processes composition based on multi-agent systems , 2014, Expert Syst. Appl..

[47]  Juan M. Corchado,et al.  Reducing the Memory Size of a Fuzzy Case-Based Reasoning System Applying Rough Set Techniques , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[48]  Javier Bajo,et al.  Multi-source homogeneous data clustering for multi-target detection from cluttered background with misdetection , 2017, Appl. Soft Comput..

[49]  Céline Ehrwein Nihan Healthier? More Efficient? Fairer? An Overview of the Main Ethical Issues Raised by the Use of Ubicomp in the Workplace , 2013 .

[50]  Belén Pérez Lancho,et al.  MISIA: Middleware Infrastructure to Simulate Intelligent Agents , 2011, DCAI.

[51]  Trevor Darrell,et al.  Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.