A Fall Detection/Recognition System and an Empirical Study of Gradient-Based Feature Extraction Approaches

Physically falling down amongst the elder helpless party is one of the most intractable issues in the era of ageing society, which has attracted intensive attentions in academia ranging from clinical research to computer vision studies. This paper proposes a fall detection/recognition system within the realm of computer vision. The proposed system integrates a group of gradient-based local visual feature extraction approaches, including histogram of oriented gradients (HOG), histogram of motion gradients (HMG), histogram of optical flow (HOF), and motion boundary histograms (MBH). A comparative study of the descriptors with the support of an artificial neural network was conducted based on an in-house captured dataset. The experimental results demonstrated the effectiveness of the proposed system and the power of these descriptors in real-world applications.

[1]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[2]  I. Jolliffe.  Principal Component Analysis , 2002 .

[3]  Qiang Shen,et al.  Are More Features Better? A Response to Attributes Reduction Using Fuzzy Rough Sets , 2009, IEEE Transactions on Fuzzy Systems.

[4]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[5]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[6]  Martin Kampel,et al.  Introducing the use of depth data for fall detection , 2013, Personal and Ubiquitous Computing.

[7]  Ahmad Lotfi,et al.  Video Based Fall Detection with Enhanced Motion History Images , 2016, PETRA.

[8]  Youssef Iraqi,et al.  Fall Detection Systems for Elderly Care: A Survey , 2014, 2014 6th International Conference on New Technologies, Mobility and Security (NTMS).

[9]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[10]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[11]  Yang Liu,et al.  Parallelizing Backpropagation Neural Network Using MapReduce and Cascading Model , 2016, Comput. Intell. Neurosci..

[12]  Nicu Sebe,et al.  Video classification with Densely extracted HOG/HOF/MBH features: an evaluation of the accuracy/computational efficiency trade-off , 2015, International Journal of Multimedia Information Retrieval.

[13]  Nadia Baha,et al.  Depth camera based fall detection using human shape and movement , 2016, 2016 IEEE International Conference on Signal and Image Processing (ICSIP).

[14]  Wei Sun,et al.  A Survey of Fall Detection Algorithm for Elderly Health Monitoring , 2015, 2015 IEEE Fifth International Conference on Big Data and Cloud Computing.

[15]  Andrew Zisserman,et al.  Three things everyone should know to improve object retrieval , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Yang Ke-jian,et al.  An Image Scaling Algorithm Based on Bilinear Interpolation with VC , 2008 .

[17]  Jacques de Villiers,et al.  Backpropagation neural nets with one and two hidden layers , 1993, IEEE Trans. Neural Networks.

[18]  Miguel A. Labrador,et al.  Survey on Fall Detection and Fall Prevention Using Wearable and External Sensors , 2014, Sensors.

[19]  Vassilis Athitsos,et al.  A survey on vision-based fall detection , 2015, PETRA.

[20]  Nicu Sebe,et al.  Histograms of Motion Gradients for real-time video classification , 2016, 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI).

[21]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  E. G. Rajan,et al.  Rajan Transform and its uses in Pattern Recognition , 2009, Informatica.

[23]  Israel Gannot,et al.  Fall detection of elderly through floor vibrations and sound , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[24]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[25]  Waleed Al-Nuaimy,et al.  Human activity classification using a single accelerometer , 2014, 2014 14th UK Workshop on Computational Intelligence (UKCI).

[26]  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.

[27]  K. Samsudin,et al.  Evaluation of fall detection classification approaches , 2012, 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012).

[28]  M. Popescu,et al.  Acoustic fall detection using one-class classifiers , 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

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

[30]  H.C. Kim,et al.  Development of novel algorithm and real-time monitoring ambulatory system using Bluetooth module for fall detection in the elderly , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[31]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[32]  Mitja Lustrek,et al.  Fall Detection and Activity Recognition with Machine Learning , 2009, Informatica.

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

[34]  Fatih Murat Porikli,et al.  Changedetection.net: A new change detection benchmark dataset , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[35]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[36]  Heinz Jäckel,et al.  SPEEDY:a fall detector in a wrist watch , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..