Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution
Fall Detection With Multiple Cameras: An Occlusion-Resistant Method Based on 3-D Silhouette Vertical Distribution
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DOI：
10.1109/titb.2010.2087385
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发表时间：
2011-03-01
影响因子：
--
通讯作者：
Meunier, Jean
中科院分区：
文献类型：
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作者：
Auvinet, Edouard;Multon, Franck;Meunier, Jean
根据工业化国家的人口演变，越来越多的老年人将在家中经历福尔斯，并将需要紧急服务。主要的问题是独居老人容易跌倒。为了解决这种缺乏安全性的问题，我们提出了一种新的方法来检测福尔斯在家里，基于多个摄像机网络重建的三维形状的人。通过分析沿着垂直轴的体积分布来检测跌倒事件，并且当该分布的主要部分在预定义的时间段期间异常地靠近地板时触发警报，这意味着人已经跌倒在地板上。该方法使用健康受试者的视频进行了验证，该受试者在几种摄像机配置下执行了24个真实场景，显示了22个跌倒事件和24个共同发现事件（11个蹲伏位置，9个坐姿和4个躺在沙发上的位置），并且使用四个摄像机或更多摄像机实现了99.7%的灵敏度和特异性或更好。使用图形处理单元（GPU）的实时实现在8个摄像头的情况下达到每秒10帧（fps），在3个摄像头的情况下达到每秒16帧。
According to the demographic evolution in industrialized countries, more and more elderly people will experience falls at home and will require emergency services. The main problem comes from fall-prone elderly living alone at home. To resolve this lack of safety, we propose a new method to detect falls at home, based on a multiple-cameras network for reconstructing the 3-D shape of people. Fall events are detected by analyzing the volume distribution along the vertical axis, and an alarm is triggered when the major part of this distribution is abnormally near the floor during a predefined period of time, which implies that a person has fallen on the floor. This method was validated with videos of a healthy subject who performed 24 realistic scenarios showing 22 fall events and 24 cofounding events (11 crouching position, 9 sitting position, and 4 lying on a sofa position) under several camera configurations, and achieved 99.7% sensitivity and specificity or better with four cameras or more. A real-time implementation using a graphic processing unit (GPU) reached 10 frames per second (fps) with 8 cameras, and 16 fps with 3 cameras.