[1]王亚飞,杨庚,李百惠.基于内距离形状上下文的跌倒检测方法[J].计算机技术与发展,2014,24(03):58-62.
　WANG Ya-fei,YANG Geng,LI Bai-hui.Fall Detection Approach Based on Inner-distance Shape Context[J].,2014,24(03):58-62.
点击复制

基于内距离形状上下文的跌倒检测方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年03期
页码:
58-62
栏目:
智能、算法、系统工程
出版日期:
2014-03-31

文章信息/Info

Title:
Fall Detection Approach Based on Inner-distance Shape Context
文章编号:
1673-629X（2014）03-0058-05
作者:
王亚飞; 杨庚; 李百惠
南京邮电大学 计算机学院
Author(s):
WANG Ya-fei; YANG Geng; LI Bai-hui
关键词:
跌倒检测; 形状上下文; 动态时间规整
Keywords:
fall detection; inner distance shape context; dynamic time warping
分类号:
TP301
文献标志码:
A
摘要:
在全球社会老龄化的大背景下,老年人的身体健康状况和晚年生活质量需要更多的关注。跌倒在老年人群中发生率高并且带来的后果比较严重。文中提出一种应用于家庭场景的基于Inner-Distance形状上下文( Inner-Distance Shape Context,IDSC)的跌倒检测方法。该方法通过Inner-Distance形状上下文获得视频帧前景形状的描述信息,使用形状匹配方法对视频序列中人体形状变化进行量化。对形变量化信息使用动态时间规整( Dynamic Time Warping,DTW)方法实现跌倒行为的判定。实验结果表明该方法可有效、快速地判断跌倒,提取结果具有较好的查准率和查全率。
Abstract:
Faced with the growing population of seniors,the society needs to pay more attention to the life quality and health condition for seniors. What's more,falls in the elderly population have a high incidence and a more serious consequence. An adaptive fall detection ap-proach based on inner distance shape context in home environment is presented. This method is based on analyzing human shape deforma-tion during a video sequence. A inner distance shape context method is used to track the person's silhouette along the video sequence. The shape deformation is then quantified from these silhouettes based on shape analysis methods. Finally,falls are detected from normal activities using dynamic time warping methods. Experiments show that the fall detection approach proposed can detect the falls effectively and rapidly,the results have good precision and recall ratio.

相似文献/References:

[1]王国桢,方贤勇. 基于图模型的姿态分割估计方法[J].计算机技术与发展,2016,26(12):53.
　WANG Guo-zhen,FANG Xian-yong. Pose Segmentation and Estimation Based on Pictorial Structure Model[J].,2016,26(03):53.
[2]袁鹏泰,刘宁钟.基于关节点提取的老年人跌倒检测算法[J].计算机技术与发展,2019,29(09):200.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 038]
　YUAN Peng-tai,LIU Ning-zhong.Elderly Fall Detection Algorithm Based on Joint Point Extraction[J].,2019,29(03):200.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 038]
[3]刘 勃,孔韦韦,肖家钦,等.基于 RBF 神经网络的跌倒检测算法[J].计算机技术与发展,2022,32(06):167.[doi:10. 3969 / j. issn. 1673-629X. 2022. 06. 028]
　LIU Bo,KONG Wei-wei,XIAO Jia-qin,et al.Fall Detection Algorithm Based on RBF Neural Network[J].,2022,32(03):167.[doi:10. 3969 / j. issn. 1673-629X. 2022. 06. 028]

更新日期/Last Update: 1900-01-01