Thermal-Depth Fusion for Occluded Body Skeletal Posture Estimation
Thermal-Depth Fusion for Occluded Body Skeletal Posture Estimation
复制标题
用于闭塞身体骨骼姿势估计的热深度融合
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Min
中科院分区：
文献类型：
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作者：
S. Transue;Phuc Nguyen;Tam N. Vu;Min
对于基于视觉的监测技术来说，可靠的闭塞骨骼姿态估计是一个极具挑战性的问题。这是由于现有的基于深度的姿势估计技术所带来的一些与成像相关的挑战，当成像设备和患者之间的视线被遮挡材料遮挡时，这些技术无法提供准确的关节位置估计。在这项工作中，我们提出了一种通过体积建模使用深度和热成像来估计闭塞应用中骨骼姿势的新方法，并引入了一种受现代运动捕捉解决方案启发的新的闭塞地面真实跟踪方法。使用这种集成的体积模型，我们利用卷积神经网络来表征和识别体积热分布，这些分布与训练过的骨骼姿势估计相匹配，包括不连贯的骨骼定义，并允许在高度模糊的情况下进行正确的姿势估计。我们通过正确识别常见的睡眠姿势来证明这种方法，这些姿势对当前的骨骼关节估计具有挑战性，获得了约94.45%的平均分类准确率。
Reliable occluded skeletal posture estimation is a fundamentally challenging problem for vision-based monitoring techniques. This is due to several imaging related challenges introduced by existing depth-based pose estimation techniques that fail to provide accurate joint position estimates when the line of sight between the imaging device and the patient is obscured by an occluding material. In this work, we present a new method of estimating skeletal posture in occluded applications using both depth and thermal imaging through volumetric modeling and introduce a new occluded ground-truth tracking method inspired by modern motion capture solutions. Using this integrated volumetric model, we utilize Convolutional Neural Networks to characterize and identify volumetric thermal distributions that match trained skeletal posture estimates which includes disconnected skeletal definitions and allows correct posture estimation in highly ambiguous cases. We demonstrate this approach by correctly identifying common sleep postures that present challenging cases for current skeletal joint estimations, obtaining an average classification accuracy of ~94.45%.