论文标题
基于压缩感应的秋季检测隐私
Compressive sensing based privacy for fall detection
论文作者
论文摘要
秋季检测在医疗保健领域非常重要，及时检测允许即时医疗援助。在这种情况下，我们提出了一个3D Convnet体系结构，该体系结构由3D Inception模块组成。所提出的体系结构是膨胀的3D（i3d）体系结构的自定义版本，它将视频序列作为时空输入的压缩度测量，从压缩传感框架中获得，而不是视频序列作为输入，例如I3D卷积神经网络。这是通过这些RGB摄像机监测的患者引起的，这引起了人们的巨大关注。相对于多种测量矩阵，提出的秋季检测框架足够灵活。从Kinetics-400随机选择的十个动作类别没有秋季示例，用于训练我们的3D Convnet Post压缩感测，并在原始视频片段上使用不同类型的传感矩阵进行训练。我们的结果表明，3D Convnet性能与不同的传感矩阵保持不变。同样，通过动力学预先训练的3D Convnet在基准数据集的秋季视频中获得的性能比最先进的技术更好。
Fall detection holds immense importance in the field of healthcare, where timely detection allows for instant medical assistance. In this context, we propose a 3D ConvNet architecture which consists of 3D Inception modules for fall detection. The proposed architecture is a custom version of Inflated 3D (I3D) architecture, that takes compressed measurements of video sequence as spatio-temporal input, obtained from compressive sensing framework, rather than video sequence as input, as in the case of I3D convolutional neural network. This is adopted since privacy raises a huge concern for patients being monitored through these RGB cameras. The proposed framework for fall detection is flexible enough with respect to a wide variety of measurement matrices. Ten action classes randomly selected from Kinetics-400 with no fall examples, are employed to train our 3D ConvNet post compressive sensing with different types of sensing matrices on the original video clips. Our results show that 3D ConvNet performance remains unchanged with different sensing matrices. Also, the performance obtained with Kinetics pre-trained 3D ConvNet on compressively sensed fall videos from benchmark datasets is better than the state-of-the-art techniques.
