Automatic Fall Detection Using Membership Based Histogram Descriptors

作     者：Mohamed Maher Ben Ismail Ouiem Bchir 

作者机构：College of Computer and Information Sciences King Saud University Riyadh 11495 KSA 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2017年第32卷第2期

页      面：356-367页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 

基　　金：supported by the Research Center of the College of Computer and Information Sciences  King Saud University 

主　　题：fall detection possibilistic approach feature extraction clustering 

摘      要：t 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 arid 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.