ActiSight: Wearer Foreground Extraction Using a Practical RGB-Thermal Wearable.
ActiSight: Wearer Foreground Extraction Using a Practical RGB-Thermal Wearable.
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ActiSight：使用实用的 RGB 热可穿戴设备提取佩戴者前景。
DOI：
10.1109/percom53586.2022.9762385
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Hester,Josiah
中科院分区：
文献类型：
--
作者：
Alharbi,Rawan;Sen,Sougata;Ng,Ada;Alshurafa,Nabil;Hester,Josiah
可穿戴式相机提供佩戴者活动、背景和互动的信息视图。从可穿戴式摄像头获得的视频对于生活日志、人类活动识别、视觉确认和其他在当今移动计算中广泛使用的任务非常有用。提取与佩戴者相关的前景信息和分离无关的背景像素是这些任务的基本操作。然而，目前仅依赖图像数据的佩戴者前景提取方法速度慢、能量效率低，在某些情况下甚至不准确，使得许多任务--如活动识别--在缺乏大量计算资源的情况下难以实现。为了填补这一空白，我们开发了ActiSight，这是一款可穿戴的RGB热敏摄像头，它利用热量信息使佩戴者的分割适用于人体视频。使用ActiSight，我们从6名参与者那里收集了总共59小时的视频，捕捉到了自然环境中的各种活动。实验表明，与仅使用RGB的方法相比，使用ActiSight提取的佩戴者前景获得了较高的骰子相似度，同时显著降低了执行时间和能量成本。
Wearable cameras provide an informative view of wearer activities, context, and interactions. Video obtained from wearable cameras is useful for life-logging, human activity recognition, visual confirmation, and other tasks widely utilized in mobile computing today. Extracting foreground information related to the wearer and separating irrelevant background pixels is the fundamental operation underlying these tasks. However, current wearer foreground extraction methods that depend on image data alone are slow, energy-inefficient, and even inaccurate in some cases, making many tasks–like activity recognition–challenging to implement in the absence of significant computational resources. To fill this gap, we built ActiSight, a wearable RGB-Thermal video camera that uses thermal information to make wearer segmentation practical for body-worn video. Using ActiSight, we collected a total of 59 hours of video from 6 participants, capturing a wide variety of activities in a natural setting. We show that wearer foreground extracted with ActiSight achieves a high dice similarity score while significantly lowering execution time and energy cost when compared with an RGB-only approach.