›› 2017, Vol. 32 ›› Issue (2): 356-367.doi: 10.1007/s11390-017-1725-z

Special Issue: Artificial Intelligence and Pattern Recognition

• Artificial Intelligence and Pattern Recognition • Previous Articles     Next Articles

Automatic Fall Detection Using Membership Based Histogram Descriptors

Mohamed Maher Ben Ismail, Ouiem Bchir   

  1. College of Computer and Information Sciences, King Saud University, Riyadh 11495, KSA
  • Received:2016-02-23 Revised:2016-11-20 Online:2017-03-05 Published:2017-03-05
  • About author:Mohamed Maher Ben Ismail is an assistant professor at the Computer Science Department of the College of Computer and Information Sciences at King Saud University, Riyadh. He received his Ph.D. degree in computer science and engineering from the University of Louisville, KY, USA, in 2011. His research interests include pattern recognition, machine learning, data mining and image processing.
  • Supported by:

    This work was supported by the Research Center of the College of Computer and Information Sciences, King Saud University.

We propose a framework for automatic fall detection based on video visual feature extraction. The proposed approach relies on a membership histogram descriptor that encodes the visual properties of the video frames. This descriptor is obtained by mapping the original low-level visual features to a more discriminative descriptor using possibilistic memberships. This mapping can be summarized in two main phases. The first one consists in categorizing the low-level visual features of the video frames and generating homogeneous clusters in an unsupervised way. The second phase uses the obtained membership degrees generated by the clustering process to compute the membership based histogram descriptor (MHD). For the testing stage, the proposed fall detection approach categorizes unlabeled videos as "Fall" or "Non-Fall" scene using a possibilistic K-nearest neighbors classifier. The proposed approach is assessed using standard videos dataset that simulates patient fall. Also, we compare its performance with that of state-of-the-art fall detection techniques.

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