A survey on vision-based fall detection

Falls are a major cause of fatal injury for the elderly population. To improve the quality of living for seniors, a wide range of monitoring systems with fall detection functionality have been proposed over recent years. This article is a survey of systems and algorithms which aim at automatically detecting cases where a human falls and may have been injured. Existing fall detection methods can be categorized as using sensors, or being exclusively vision-based. This literature review focuses on vision-based methods.

[1]  Stephen J. McKenna,et al.  Activity summarisation and fall detection in a supportive home environment , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[2]  Yap-Peng Tan,et al.  Fall Incidents Detection for Intelligent Video Surveillance , 2005, 2005 5th International Conference on Information Communications & Signal Processing.

[3]  C. Rougier,et al.  Monocular 3D Head Tracking to Detect Falls of Elderly People , 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

[4]  S. Miaou,et al.  A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information , 2006, 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, 2006. D2H2..

[5]  Sabu Emmanuel,et al.  Intelligent Video Surveillance for Monitoring Elderly in Home Environments , 2007, 2007 IEEE 9th Workshop on Multimedia Signal Processing.

[6]  Rita Cucchiara,et al.  A multi‐camera vision system for fall detection and alarm generation , 2007, Expert Syst. J. Knowl. Eng..

[7]  Marjorie Skubic,et al.  An acoustic fall detector system that uses sound height information to reduce the false alarm rate , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[8]  Nicolas Thome,et al.  A Real-Time, Multiview Fall Detection System: A LHMM-Based Approach , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[9]  H. Foroughi,et al.  An eigenspace-based approach for human fall detection using Integrated Time Motion Image and Neural Network , 2008, 2008 9th International Conference on Signal Processing.

[10]  Lionel Reveret,et al.  Fall detection using multiple cameras , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[11]  James M. Keller,et al.  Modeling Human Activity From Voxel Person Using Fuzzy Logic , 2009, IEEE Transactions on Fuzzy Systems.

[12]  Luc Van Gool,et al.  Tracker trees for unusual event detection , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

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

[14]  Mark Hasegawa-Johnson,et al.  Acoustic fall detection using Gaussian mixture models and GMM supervectors , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[15]  Pietro Siciliano,et al.  An active vision system for fall detection and posture recognition in elderly healthcare , 2010, 2010 Design, Automation & Test in Europe Conference & Exhibition (DATE 2010).

[16]  V. Athitsos,et al.  Experiments with computer vision methods for fall detection , 2010, PETRA '10.

[17]  Martin Kampel,et al.  Introducing a Statistical Behavior Model into Camera-Based Fall Detection , 2010, ISVC.

[18]  Franck Multon,et al.  Fall Detection Using Body Volume Recontruction and Vertical Repartition Analysis , 2010, ICISP.

[19]  Franck Multon,et al.  Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution , 2011, IEEE Transactions on Information Technology in Biomedicine.

[20]  Max Mignotte,et al.  Fall Detection from Depth Map Video Sequences , 2011, ICOST.

[21]  Alessandro Leone,et al.  Detecting falls with 3D range camera in ambient assisted living applications: a preliminary study. , 2011, Medical engineering & physics.

[22]  Jean Meunier,et al.  Robust Video Surveillance for Fall Detection Based on Human Shape Deformation , 2011, IEEE Transactions on Circuits and Systems for Video Technology.

[23]  Vangelis Metsis,et al.  A viewpoint-independent statistical method for fall detection , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[24]  Rached Tourki,et al.  Definition and Performance Evaluation of a Robust SVM Based Fall Detection Solution , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.

[25]  Yingli Tian,et al.  Privacy Preserving Automatic Fall Detection for Elderly Using RGBD Cameras , 2012, ICCHP.

[26]  Dimitrios Makris,et al.  Fall detection system using Kinect’s infrared sensor , 2014, Journal of Real-Time Image Processing.

[27]  Wan-Young Chung,et al.  Visual Sensor Based Abnormal Event Detection with Moving Shadow Removal in Home Healthcare Applications , 2012, Sensors.

[28]  Chung-Lin Huang,et al.  Slip and fall event detection using Bayesian Belief Network , 2012, Pattern Recognit..

[29]  Hideo Saito,et al.  Detecting Fall Incidents of the Elderly Based on Human-Ground Contact Areas , 2013, 2013 2nd IAPR Asian Conference on Pattern Recognition.

[30]  Ling Shao,et al.  A survey on fall detection: Principles and approaches , 2013, Neurocomputing.

[31]  Tuan V. Pham,et al.  Human fall detection based on adaptive background mixture model and HMM , 2013, 2013 International Conference on Advanced Technologies for Communications (ATC 2013).

[32]  Rached Tourki,et al.  Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification , 2013, J. Electronic Imaging.

[33]  Inmaculada Plaza,et al.  Challenges, issues and trends in fall detection systems , 2013, Biomedical engineering online.

[34]  Hideo Saito,et al.  The Estimation of Heights and Occupied Areas of Humans from Two Orthogonal Views for Fall Detection , 2013 .

[35]  Nader Karimi,et al.  Automatic Monocular System for Human Fall Detection Based on Variations in Silhouette Area , 2013, IEEE Transactions on Biomedical Engineering.

[36]  Rui Liu,et al.  Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera , 2014, Signal Image Video Process..

[37]  Mohamed Atri,et al.  Robust spatio-temporal descriptors for real-time SVM-based fall detection , 2014, 2014 World Symposium on Computer Applications & Research (WSCAR).

[38]  Vassilis Athitsos,et al.  Evaluating Depth-Based Computer Vision Methods for Fall Detection under Occlusions , 2014, ISVC.

[39]  Haibo Wang,et al.  Depth-Based Human Fall Detection via Shape Features and Improved Extreme Learning Machine , 2014, IEEE Journal of Biomedical and Health Informatics.

[40]  Bogdan Kwolek,et al.  Fall detection using ceiling-mounted 3D depth camera , 2015, 2014 International Conference on Computer Vision Theory and Applications (VISAPP).

[41]  Marjorie Skubic,et al.  Fall Detection in Homes of Older Adults Using the Microsoft Kinect , 2015, IEEE Journal of Biomedical and Health Informatics.

[42]  Haibo Wang,et al.  Shape feature encoding via Fisher Vector for efficient fall detection in depth-videos , 2015, Appl. Soft Comput..

[43]  Nadia Magnenat-Thalmann,et al.  Fall Detection Based on Body Part Tracking Using a Depth Camera , 2015, IEEE Journal of Biomedical and Health Informatics.