ZenCam: Context-Driven Control of Autonomous Body Cameras
ZenCam: Context-Driven Control of Autonomous Body Cameras
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DOI：
10.1109/dcoss.2019.00029
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
Shiwei Fang;Ketan Mayer-Patel;S. Nirjon
中科院分区：
文献类型：
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作者：
Shiwei Fang;Ketan Mayer-Patel;S. Nirjon
在本文中，我们提出- ZenCam，这是一个始终在线的身体摄像头，利用现成的信息，从片上固件的编码视频流中的动态场景进行分类。该场景上下文进一步与佩戴者的基于简单惯性测量单元（IMU）的活动水平上下文组合，以在运行时最佳地控制相机配置，从而将设备保持在期望的能量预算之下。我们描述了ZenCam的设计和实现，并在现实世界的场景中彻底评估其性能。我们的评估显示，与30 fps的1920 x1080标准基线设置相比，能耗降低了29.8-35%，存储使用量降低了48.1-49.5%，同时以最小的计算开销保持了具有竞争力或更好的视频质量。
In this paper, we present — ZenCam, which is an always-on body camera that exploits readily available information in the encoded video stream from the on-chip firmware to classify the dynamics of the scene. This scene-context is further combined with simple inertial measurement unit (IMU)-based activity level-context of the wearer to optimally control the camera configuration at run-time to keep the device under the desired energy budget. We describe the design and implementation of ZenCam and thoroughly evaluate its performance in real-world scenarios. Our evaluation shows a 29.8-35% reduction in energy consumption and 48.1-49.5% reduction in storage usage when compared to a standard baseline setting of 1920x1080 at 30fps while maintaining a competitive or better video quality at the minimal computational overhead.