基于多摄像头监控的人体跌倒检测算法
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摘要
人口老龄化使得空巢老人数量越来越多。跌倒作为威胁独居老人生命安全的主要因素受到了社会的广泛关注。为保护老人的生命健康不受威胁,本文提出一种基于计算机视觉的两级人体跌倒检测算法。从监控摄像机中采集视频数据,对其做前景提取,通过形态学操作为前景块绘制矩形边界,根据矩形宽高比从中筛选出所有可能是跌倒的行为,这是粗粒度级检测。之后再用统计学方法对第一级检测出的前景块绘制椭圆边界,分析其形态变化,最终检测出跌倒行为,这是细粒度级检测。本文在开源多摄像头跌倒数据集上进行了评估。仿真实验和与当前的先进方法的对比表明本文算法取得了非常好的效果。
The number of empty-nesters is continuously increasing due to ageing of population. As the main factor that threatens the life of empty-nesters, falls have received wide attention in society. This paper presents a dual-stage fall detection algorithm based on computer vision, which aims at protecting the life and health of elderly. Background subtraction is used to extract foreground image from surveillance video. We first draw a bounding box for the foreground blob by morphological operation, and recognize all potential falls using aspect ratio of the bounding box(coarse granularity level). After that, we draw ellipse for the foreground image of potential falls based on statistical method, and analyze variations of ellipse to achieve fall detection(fine granularity level). This paper have been conducted on a publicly available multi-camera fall dataset. Simulation experiments and comparison with state-of-the-art show the effectiveness of the proposed algorithm.
The number of empty-nesters is continuously increasing due to ageing of population. As the main factor that threatens the life of empty-nesters, falls have received wide attention in society. This paper presents a dual-stage fall detection algorithm based on computer vision, which aims at protecting the life and health of elderly. Background subtraction is used to extract foreground image from surveillance video. We first draw a bounding box for the foreground blob by morphological operation, and recognize all potential falls using aspect ratio of the bounding box(coarse granularity level). After that, we draw ellipse for the foreground image of potential falls based on statistical method, and analyze variations of ellipse to achieve fall detection(fine granularity level). This paper have been conducted on a publicly available multi-camera fall dataset. Simulation experiments and comparison with state-of-the-art show the effectiveness of the proposed algorithm.
引文
[1] 陈建梅,方锡悦.空巢老人养老保障的国际经验借鉴与启示[J].哈尔滨商业大学学报(社会科学版),2013,(5):51-57.Chen J M,Fang X Y.Draw on the international experience of empty nester security[J].Journal of Harbin University of Commerce,2013.(5):51-57.
[2] Yu X.Approaches and principles of fall detection for elderly and patient[C]//International Conference on E-health Networking,Application and Services.Singapore:IEEE,2008:42-47.
[3] 王荣,章韵,陈建新.基于三轴加速度传感器的人体跌倒检测系统设计与实现[J].计算机应用,2012,(5):1450-1452.Rong W,Yun Z,Chen J X.Design and implementation of fall detection system using tri-axis accelerometer[J].Journal of Computer Applications,2012,32(5):1450-1452.
[4] Shibuya N,Nukala B T,Rodriguez A I,et al.A real-time fall detection system using a wearable gait analysis sensor and a support vector machine (svm) classifier[C]//International Conference on Mobile Computing and Ubiquitous Networking.Hakodate:IEEE,2015:66-67.
[5] Abbate S,Avvenuti M,Bonatesta F,et al.A smartphone-based fall detection system[J].Pervasive and Mobile Computing,2012,8(6):883-899.
[6] Rougier C,Meunier J,St-Arnaud A,et al.Robust video surveillance for fall detection based on human shape deformation[J].IEEE Transactions on Circuits and Systems for Video Technology,2011,21(5):611-622.
[7] Mirmahboub B,Samavi S,Karimi N,et al.Automatic monocular system for human fall detection based on variations in silhouette area[J].IEEE Transactions on Biomedical Engineering,2013,60(2):427-436.
[8] Yun Y,Gu Y H.Human fall detection in videos via boosting and fusing statistical features of appearance,shape and motion dynamics on riemannian manifolds with applications to assisted living[J].Computer Vision & Image Understanding,2016,148:111-122.
[9] Miaou S G,Sung P H,Huang C Y.A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information[C]//Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare.Arlington:IEEE,2006:39-42.
[10] Vishwakarma V,Mandal C,Sural S.Automatic Detection of Human Fall in Video[C]//International Conference on Pattern Recognition and Machine Intelligence.Kolkata:Springer,2007:616-623.
[11] Wren C R,Azarbayejani A,Darrell T,et al.Pfinder:Real-time tracking of the human body[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):780-785.
[12] He L,Chao Y,Suzuki K,et al.Fast connected-component labeling[J].Pattern Recognition,2009,42(9):1977-1987.
[13] Rougier C,Meunier J,St-Arnaud A,et al.Fall detection from human shape and motion history using video surveillance[C]//Advanced Information Networking and Applications Workshops.Niagara Falls:IEEE,2007:875-880.
[14] Foroughi H,Aski B S,Pourreza H.Intelligent video surveillance for monitoring fall detection of elderly in home environments[C]//International Conference on Computer and Information Technology.Khulna:IEEE,2008:219-224.
[15] Auvinet E,Rougier C,Meunier J,et al.Multiple Cameras Fall Dataset[R].Quebec City:DIRO-Universide Montreal,2010.
[16] Auvinet E,Multon F,Saint-Arnaud A,et al.Fall detection with multiple cameras:An occlusion-resistant method based on 3-d silhouette vertical distribution[J].IEEE Transactions on Information Technology in Biomedicine,2011,15(2):290-300.
[17] Wang S,Chen L,Zhou Z,et al.Human fall detection in surveillance video based on PCANet[J].Multimedia Tools and Applications,2016,75(19):11603-11613.
[2] Yu X.Approaches and principles of fall detection for elderly and patient[C]//International Conference on E-health Networking,Application and Services.Singapore:IEEE,2008:42-47.
[3] 王荣,章韵,陈建新.基于三轴加速度传感器的人体跌倒检测系统设计与实现[J].计算机应用,2012,(5):1450-1452.Rong W,Yun Z,Chen J X.Design and implementation of fall detection system using tri-axis accelerometer[J].Journal of Computer Applications,2012,32(5):1450-1452.
[4] Shibuya N,Nukala B T,Rodriguez A I,et al.A real-time fall detection system using a wearable gait analysis sensor and a support vector machine (svm) classifier[C]//International Conference on Mobile Computing and Ubiquitous Networking.Hakodate:IEEE,2015:66-67.
[5] Abbate S,Avvenuti M,Bonatesta F,et al.A smartphone-based fall detection system[J].Pervasive and Mobile Computing,2012,8(6):883-899.
[6] Rougier C,Meunier J,St-Arnaud A,et al.Robust video surveillance for fall detection based on human shape deformation[J].IEEE Transactions on Circuits and Systems for Video Technology,2011,21(5):611-622.
[7] Mirmahboub B,Samavi S,Karimi N,et al.Automatic monocular system for human fall detection based on variations in silhouette area[J].IEEE Transactions on Biomedical Engineering,2013,60(2):427-436.
[8] Yun Y,Gu Y H.Human fall detection in videos via boosting and fusing statistical features of appearance,shape and motion dynamics on riemannian manifolds with applications to assisted living[J].Computer Vision & Image Understanding,2016,148:111-122.
[9] Miaou S G,Sung P H,Huang C Y.A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information[C]//Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare.Arlington:IEEE,2006:39-42.
[10] Vishwakarma V,Mandal C,Sural S.Automatic Detection of Human Fall in Video[C]//International Conference on Pattern Recognition and Machine Intelligence.Kolkata:Springer,2007:616-623.
[11] Wren C R,Azarbayejani A,Darrell T,et al.Pfinder:Real-time tracking of the human body[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1997,19(7):780-785.
[12] He L,Chao Y,Suzuki K,et al.Fast connected-component labeling[J].Pattern Recognition,2009,42(9):1977-1987.
[13] Rougier C,Meunier J,St-Arnaud A,et al.Fall detection from human shape and motion history using video surveillance[C]//Advanced Information Networking and Applications Workshops.Niagara Falls:IEEE,2007:875-880.
[14] Foroughi H,Aski B S,Pourreza H.Intelligent video surveillance for monitoring fall detection of elderly in home environments[C]//International Conference on Computer and Information Technology.Khulna:IEEE,2008:219-224.
[15] Auvinet E,Rougier C,Meunier J,et al.Multiple Cameras Fall Dataset[R].Quebec City:DIRO-Universide Montreal,2010.
[16] Auvinet E,Multon F,Saint-Arnaud A,et al.Fall detection with multiple cameras:An occlusion-resistant method based on 3-d silhouette vertical distribution[J].IEEE Transactions on Information Technology in Biomedicine,2011,15(2):290-300.
[17] Wang S,Chen L,Zhou Z,et al.Human fall detection in surveillance video based on PCANet[J].Multimedia Tools and Applications,2016,75(19):11603-11613.
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