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首页> 外文期刊>Machine Vision and Applications >Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly
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Action detection fusing multiple Kinects and a WIMU: an application to in-home assistive technology for the elderly

机译：融合多种Kinect和WIMU的动作检测：在老年人居家辅助技术中的应用

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摘要

We present a vision-inertial system which combines two RGB-Depth devices together with a wearable inertial movement unit in order to detect activities of the daily living. From multi-view videos, we extract dense trajectories enriched with a histogram of normals description computed from the depth cue and bag them into multi-view codebooks. During the later classification step a multi-class support vector machine with a RBF- $$mathcal {X}^2$$ X 2 kernel combines the descriptions at kernel level. In order to perform action detection from the videos, a sliding window approach is utilized. On the other hand, we extract accelerations, rotation angles, and jerk features from the inertial data collected by the wearable placed on the user’s dominant wrist. During gesture spotting, a dynamic time warping is applied and the aligning costs to a set of pre-selected gesture sub-classes are thresholded to determine possible detections. The outputs of the two modules are combined in a late-fusion fashion. The system is validated in a real-case scenario with elderly from an elder home. Learning-based fusion results improve the ones from the single modalities, demonstrating the success of such multimodal approach.
机译：我们提出了一种视觉惯性系统，该系统将两个RGB深度设备与可穿戴的惯性运动单元结合在一起，以检测日常生活活动。从多视图视频中，我们提取出密​​集的轨迹，这些轨迹丰富了根据深度提示计算出的法线描述直方图，并将其放入多视图码本中。在后面的分类步骤中，带有RBF- $$ {X} ^ 2 $$ X 2内核的多类支持向量机在内核级别组合了描述。为了从视频执行动作检测，使用了滑动窗口方法。另一方面，我们从放置在用户主要手腕上的可穿戴设备收集的惯性数据中提取加速度，旋转角度和加加速度特性。在手势识别期间，将应用动态时间扭曲，并且将对一组预选手势子类的对齐成本进行阈值确定可能的检测。这两个模块的输出以后期融合的方式组合在一起。该系统已在实际案例中与老人院中的老人一起进行了验证。基于学习的融合结果改进了单模态方法，证明了这种多模态方法的成功。

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