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首页> 外文会议>International Conference on Image Processing Theory, Tools and Applications >Fall Detection using Body Geometry in Video Sequences
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Fall Detection using Body Geometry in Video Sequences

机译：使用体重在视频序列中的坠落检测

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摘要

According to the World Health Organization, falling of the elderly is a major health problem that causes many injuries and thousands of deaths every year. This increases pressure on health authorities to provide daily health care, reliable medical assistance, reduce fall damages and improve the elderly quality of life. For that, it is a priority to detect or predict falls accurately. In this paper, we present a fall detection approach based on human body geometry inferred from video sequence frames. We calculate the angular information between the vector formed by the head centroid of the identified facial image and the center hip of the body and the vector aligned with the horizontal axis of the center hip. Similarly, we calculate the distance between the vector formed by the head and the body center hip and the vector formed on its horizontal axis; we then construct distinctive image features. These angles and distances are then used to train a two-class SVM classifier and a Long Short-Term Memory network (LSTM) on the calculated angle sequences to classify falls and no-falls activities. We perform experiments on the Le2i fall detection dataset. The results demonstrate the effectiveness and efficiency of the developed approach.
机译：根据世界卫生组织的说法，老年人的堕落是一个主要的健康问题，导致每年有许多伤害和数千人死亡。这增加了卫生当局的压力，以提供日常医疗保健，可靠的医疗援助，减少堕落损害，提高老人的生活质量。为此，它是检测或预测准确下降的优先事项。在本文中，我们介绍了一种基于从视频序列帧推断的人体几何形状的下降检测方法。我们计算由所识别的面部图像的头部质心和主体的中心臀部形成的矢量之间的角度信息和与中心臀部的水平轴对齐的矢量。类似地，我们计算由头部和主体中心臀部形成的载体之间的距离和在其横轴上形成的载体;然后我们构建独特的图像特征。然后使用这些角度和距离来训练两类SVM分类器和在计算的角度序列上的长短期存储器网络（LSTM），以对跌倒和无级活动进行分类。我们在LE2I秋季检测数据集上执行实验。结果表明了开发方法的有效性和效率。

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