Fall detection method based on semi-contour distances

被引:0
作者
Xu ShanShan [1 ]
Cai Xi [1 ]
机构
[1] Northeastern Univ Qinhuangdao, Sch Comp & Commun Engn, Qinhuangdao, Hebei, Peoples R China
来源
PROCEEDINGS OF 2018 14TH IEEE INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING (ICSP) | 2018年
基金
中国国家自然科学基金;
关键词
fall detection; computer vision; semi-contour distances; SVM classifier; VECTOR;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
In recent decades, as aging population and empty-nest families are increasing, falls in the elderly have become the health care issues that cannot be neglected. The computer vision-based methods are promising for fall detection, for they apply a camera installed indoors to monitor the movement of the elderly in real time. However, the features used in previous methods lack specificity, thus they have difficulty in distinguishing falls from some normal activities, such as crouching down. In this paper, we propose a novel fall detection method based on semi-contour distances for monitoring elderly people in the home environment. Firstly, we use the Gaussian Mixed Model (GMM) method to extract the human silhouette; then capture the geometric features, i.e. the semi-contour distances of the human silhouettes; finally apply the SVM classifiers to classify different actions to complete fall detection. The experimental results demonstrate that the accuracy of this algorithm is 86.79% and the sensitivity is 96.87%. It is shown that the proposed video-based fall detection method could achieve a reliable result.
引用
收藏
页码:785 / 788
页数:4
相关论文
共 15 条
[1]  
[Anonymous], 2007, GOHNET NEWS LETT, P1
[2]   Shape feature encoding via Fisher Vector for efficient fall detection in depth-videos [J].
Aslan, Muzaffer ;
Sengur, Abdulkadir ;
Xiao, Yang ;
Wang, Haibo ;
Ince, M. Cevdet ;
Ma, Xin .
APPLIED SOFT COMPUTING, 2015, 37 :1023-1028
[3]   Fall detection based on the gravity vector using a wide-angle camera [J].
Bosch-Jorge, Marc ;
Sanchez-Salmeron, Antonio-Jose ;
Valera, Angel ;
Ricolfe-Viala, Carlos .
EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (17) :7980-7986
[4]   A simple vision-based fall detection technique for indoor video surveillance [J].
Chua, Jia-Luen ;
Chang, Yoong Choon ;
Lim, Wee Keong .
SIGNAL IMAGE AND VIDEO PROCESSING, 2015, 9 (03) :623-633
[5]   Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera [J].
Feng, Weiguo ;
Liu, Rui ;
Zhu, Ming .
SIGNAL IMAGE AND VIDEO PROCESSING, 2014, 8 (06) :1129-1138
[6]   Falls and fear of falling: Which comes first? A longitudinal prediction model suggests strategies for primary and secondary prevention [J].
Friedman, SM ;
Munoz, B ;
West, SK ;
Rubin, GS ;
Fried, LP .
JOURNAL OF THE AMERICAN GERIATRICS SOCIETY, 2002, 50 (08) :1329-1335
[7]  
HelpAge International, 2015, GLOB AG SURV IND
[8]   Pedestrian Fall Action Detection and Alarm in Video Surveillance [J].
Hou, Yan-rong ;
Wang, Jin-xiang .
2016 3RD INTERNATIONAL CONFERENCE ON INFORMATION SCIENCE AND CONTROL ENGINEERING (ICISCE), 2016, :502-505
[9]  
Kaewtrakulpong P., 2001, IMPROVED ADAPTIVE BA, P135
[10]   Monitoring behavior in home using a smart fall sensor and position sensors [J].
Noury, N ;
Hervé, T ;
Rialle, V ;
Virone, G ;
Mercier, E ;
Morey, G ;
Moro, A ;
Porcheron, T .
1ST ANNUAL INTERNATIONAL IEEE-EMBS SPECIAL TOPIC CONFERENCE ON MICROTECHNOLOGIES IN MEDICINE & BIOLOGY, PROCEEDINGS, 2000, :607-610