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Evaluating Depth-Based Computer Vision Methods for Fall Detection under Occlusions

机译：评估基于深度的计算机视觉方法在遮挡下的跌倒检测

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Falls are one of the major risks for seniors living alone at home. Fall detection has been widely studied in the computer vision community, especially since the advent of affordable depth sensing technology like the Kinect. Most existing methods assume that the whole fall process is visible to the camera. This is not always the case, however, since the end of the fall can be completely occluded by a certain object, like a bed. For a system to be usable in real life, the occlusion problem must be addressed. To quantify the challenges and assess performance in this topic, we present an occluded fall detection benchmark dataset containing 60 occluded falls for which the end of the fall is completely occluded. We also evaluate four existing fall detection methods using a single depth camera on this benchmark dataset.
机译：跌落是老年人独自在家中的主要风险之一。跌倒检测在计算机视觉界已得到广泛研究，尤其是自从出现了可承受的深度感应技术（如Kinect）以来。现有的大多数方法都假定整个跌落过程对相机都是可见的。然而，情况并非总是如此，因为跌倒的结束可能完全被某些物体（例如床）所遮挡。为了使系统在现实生活中可用，必须解决遮挡问题。为了量化该主题中的挑战并评估性能，我们提出了一个包含60个被遮挡的跌倒的被遮挡的跌倒检测基准数据集，对于这些跌落，其跌落的终点被完全遮挡了。我们还在此基准数据集上使用单个深度相机评估了四种现有的跌倒检测方法。

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