Implementation of an Integrated Ambient Intelligence System

We present an Integrated Ambient Intelligence System to perceive the presence of people, identify them, determine their locations, and provide suitable interaction with them. The proposed framework can be applied in various application domains such as a smart house, robotics, authorization, surveillance, crime prevention and many others. The proposed system has five components: body tracking, face recognition, controller, monitoring device, and interaction modules. The system deploys RGB cameras and Kinect depth sensors to obtain human information for execution. The proposed recognition-tracking system works at around 10Hz. The developed system is designed to be a fast and reliable system for indoor environments.

[1]  Matteo Munaro,et al.  Fast RGB-D people tracking for service robots , 2014, Auton. Robots.

[2]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Scott A. King,et al.  Creating speech-synchronized animation , 2005, IEEE Transactions on Visualization and Computer Graphics.

[5]  Diane J. Cook,et al.  Author's Personal Copy Pervasive and Mobile Computing Ambient Intelligence: Technologies, Applications, and Opportunities , 2022 .

[6]  Mahadev Satyanarayanan,et al.  OpenFace: A general-purpose face recognition library with mobile applications , 2016 .

[7]  Álvaro Marco,et al.  AmbienNet: an intelligent environment to support people with disabilities and elderly people , 2008, Assets '08.

[8]  Subhasis Chaudhuri,et al.  Gesture recognition using position and appearance features , 2003, Proceedings 2003 International Conference on Image Processing (Cat. No.03CH37429).

[9]  Dani Martínez,et al.  Ambient Intelligence Application Based on Environmental Measurements Performed with an Assistant Mobile Robot , 2014, Sensors.

[10]  Paul Müller,et al.  Ambient Intelligence in Assisted Living: Enable Elderly People to Handle Future Interfaces , 2007, HCI.

[11]  Ennio Gambi,et al.  A Depth-Based Fall Detection System Using a Kinect® Sensor , 2014, Sensors.

[12]  Anand Chandrasekhar,et al.  Ambient Intelligence: The Next Generation Technology A Review , 2011 .

[13]  Xiang Li,et al.  Reflection invariant local binary patterns for image texture classification , 2015, RACS.

[14]  Juan Carlos Augusto,et al.  Ambient intelligence: Basic concepts and applications , 2006, ICSOFT.

[15]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[16]  Scott Craver,et al.  A Negative Number Vulnerability for Histogram-based Face Recognition Systems , 2015, IH&MMSec.

[17]  Ruijiao Li,et al.  Cognitive assisted living ambient system: a survey , 2015, Digit. Commun. Networks.

[18]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Fariba Sadri,et al.  Ambient intelligence: A survey , 2011, CSUR.

[21]  James Philbin,et al.  FaceNet: A unified embedding for face recognition and clustering , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Gerhard Goos,et al.  Ambient Intelligence , 2015, Lecture Notes in Computer Science.

[23]  Hyun Myung,et al.  Gesture recognition algorithm for moving kinect sensor , 2013, 2013 IEEE RO-MAN.

[24]  Manuel Roveri,et al.  What planner for ambient intelligence applications? , 2005, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[25]  Davis E. King,et al.  Dlib-ml: A Machine Learning Toolkit , 2009, J. Mach. Learn. Res..