跌倒是老年人最严重的医疗保健风险之一,being,在一些不利的情况下,死亡的间接原因.此外,对未来的人口预测显示,全球老年人口正在增长。在这种情况下,自动跌倒检测和预测的模型具有至关重要的意义,尤其是使用环境的AI应用程序,传感器或计算机视觉。在本文中,我们提出了一种使用计算机视觉技术进行跌倒检测的方法。封闭环境中的人的视频序列用作我们算法的输入。在我们的方法中,我们首先应用V2V-PoseNet模型来检测每一帧中的2D身体骨架。具体来说,我们的方法包括四个步骤:(1)在每个帧中通过V2V-PoseNet检测身体骨骼;(2)首先将骨骼的关节映射到固定秩2的正半定矩阵的黎曼流形中,以建立时间参数化的轨迹;(3)对轨迹进行时间扭曲,提供它们之间的(不)相似性度量;(4)最后,使用成对接近函数SVM将它们分类为跌倒或非跌倒,将(不)相似性度量结合到核函数中。我们在两个公开可用的数据集URFD和Charfi上评估了我们的方法。所提出的方法的结果与最先进的方法相比具有竞争力,而只涉及2D身体骨骼。
Falls are one of the most critical health care risks for elderly people, being, in some adverse circumstances, an indirect cause of death. Furthermore, demographic forecasts for the future show a growing elderly population worldwide. In this context, models for automatic fall detection and prediction are of paramount relevance, especially AI applications that use ambient, sensors or computer vision. In this paper, we present an approach for fall detection using computer vision techniques. Video sequences of a person in a closed environment are used as inputs to our algorithm. In our approach, we first apply the V2V-PoseNet model to detect 2D body skeleton in every frame. Specifically, our approach involves four steps: (1) the body skeleton is detected by V2V-PoseNet in each frame; (2) joints of skeleton are first mapped into the Riemannian manifold of positive semidefinite matrices of fixed-rank 2 to build time-parameterized trajectories; (3) a temporal warping is performed on the trajectories, providing a (dis-)similarity measure between them; (4) finally, a pairwise proximity function SVM is used to classify them into fall or non-fall, incorporating the (dis-)similarity measure into the kernel function. We evaluated our approach on two publicly available datasets URFD and Charfi. The results of the proposed approach are competitive with respect to state-of-the-art methods, while only involving 2D body skeletons.