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首页> 外文期刊>Applied Sciences >Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation
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Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation

机译：基于视角不变的人类活动表示的部分观测深度图视频序列中的老年人跌倒检测

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This paper presents a new approach for fall detection from partially-observed depth-map video sequences. The proposed approach utilizes the 3D skeletal joint positions obtained from the Microsoft Kinect sensor to build a view-invariant descriptor for human activity representation, called the motion-pose geometric descriptor (MPGD). Furthermore, we have developed a histogram-based representation (HBR) based on the MPGD to construct a length-independent representation of the observed video subsequences. Using the constructed HBR, we formulate the fall detection problem as a posterior-maximization problem in which the posteriori probability for each observed video subsequence is estimated using a multi-class SVM (support vector machine) classifier. Then, we combine the computed posteriori probabilities from all of the observed subsequences to obtain an overall class posteriori probability of the entire partially-observed depth-map video sequence. To evaluate the performance of the proposed approach, we have utilized the Kinect sensor to record a dataset of depth-map video sequences that simulates four fall-related activities of elderly people, including: walking, sitting, falling form standing and falling from sitting. Then, using the collected dataset, we have developed three evaluation scenarios based on the number of unobserved video subsequences in the testing videos, including: fully-observed video sequence scenario, single unobserved video subsequence of random lengths scenarios and two unobserved video subsequences of random lengths scenarios. Experimental results show that the proposed approach achieved an average recognition accuracy of 93 . 6 % , 77 . 6 % and 65 . 1 % , in recognizing the activities during the first, second and third evaluation scenario, respectively. These results demonstrate the feasibility of the proposed approach to detect falls from partially-observed videos.
机译：本文提出了一种从部分观察到的深度图视频序列中进行跌倒检测的新方法。所提出的方法利用从Microsoft Kinect传感器获得的3D骨骼关节位置来构建用于人类活动表示的视图不变描述符，称为运动姿势几何描述符（MPGD）。此外，我们已经开发了基于MPGD的基于直方图的表示形式（HBR），以构建观察到的视频子序列的长度无关的表示形式。使用构造的HBR，我们将跌倒检测问题公式化为后验最大化问题，其中使用多类SVM（支持向量机）分类器估算每个观察到的视频子序列的后验概率。然后，我们结合从所有观察到的子序列计算出的后验概率，以获得整个部分观察到的深度图视频序列的整体后验概率。为了评估所提出方法的性能，我们利用Kinect传感器记录了深度图视频序列的数据集，该数据集模拟了老年人与摔倒相关的四种活动，包括：走路，坐着，从站立时跌落和从坐下跌落。然后，使用收集的数据集，根据测试视频中未观察到的视频子序列的数量，开发了三种评估方案，包括：完全观察到的视频序列方案，随机长度的单个未观察到的视频子序列方案和随机的两个未观察到的视频子序列长度方案。实验结果表明，该方法的平均识别精度为93。 6％，77。 6％和65。 1％，分别用于识别第一，第二和第三评估情景中的活动。这些结果证明了所提出的方法从部分观察到的视频中检测跌倒的可行性。

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