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首页> 外文期刊>Multi-Scale Computing Systems, IEEE Transactions on >An Ultra-Low Power, “Always-On” Camera Front-End for Posture Detection in Body Worn Cameras Using Restricted Boltzman Machines
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An Ultra-Low Power, “Always-On” Camera Front-End for Posture Detection in Body Worn Cameras Using Restricted Boltzman Machines

机译：超低功耗，“始终开启”的摄像头前端，用于使用受限的玻尔兹曼机对身体破损的摄像头进行姿势检测

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摘要

The Internet of Things (IoTs) has triggered rapid advances in sensors, surveillance devices, wearables and body area networks with advanced Human-Computer Interfaces (HCI). One such application area is the adoption of Body Worn Cameras (BWCs) by law enforcement officials. The need to be ‘always-on’ puts heavy constraints on battery usage in these camera front-ends, thus limiting their widespread adoption. Further, the increasing number of such cameras is expected to create a data deluge, which requires large processing, transmission and storage capabilities. Instead of continuously capturing and streaming or storing videos, it is prudent to provide “smartness” to the camera front-end. This requires hardware assisted image recognition and template matching in the front-end, capable of making judicious decisions on when to trigger video capture or streaming. Restricted Boltzmann Machines (RBMs) based neural networks have been shown to provide high accuracy for image recognition and are well suited for low power and re-configurable systems. In this paper we propose an RBM based “always-on’’ camera front-end capable of detecting human posture. Aggressive behavior of the human being in the field of view will be used as a wake-up signal for further data collection and classification. The proposed system has been implemented on a Xilinx Virtex 7 XC7VX485T platform. A minimum dynamic power of 19.18 mW for a target recognition accuracy while maintaining real time constraints has been measured. The hardware-software co-design illustrates the trade-offs in the design with respect to accuracy, resource utilization, processing time and power. The results demonstrate the possibility of a true “always-on” body-worn camera system in the IoT environment.
机译：物联网（IoT）通过先进的人机界面（HCI）触发了传感器，监视设备，可穿戴设备和人体局域网的快速发展。一种这样的应用领域是执法人员采用的随身摄像机（BWC）。需要“始终开机”对这些摄像机前端的电池使用量施加了严格的限制，从而限制了它们的广泛采用。此外，预计越来越多的这种相机会造成数据泛滥，这需要大量的处理，传输和存储功能。与其持续捕获，流式传输或存储视频，不如对摄像机前端提供“智能”。这需要前端中的硬件辅助图像识别和模板匹配，能够对何时触发视频捕获或流媒体做出明智的决定。基于受限玻尔兹曼机（RBM）的神经网络已显示出可提供图像识别的高精度，并且非常适合于低功耗和可重新配置的系统。在本文中，我们提出了一种基于RBM的“始终在线”相机前端，能够检测人体姿势。人类在视野中的攻击行为将被用作唤醒信号，以进行进一步的数据收集和分类。拟议的系统已在Xilinx Virtex 7 XC7VX485T平台上实现。已测量出目标识别精度为19.18 mW的最小动态功率，同时又保持了实时约束。硬件-软件协同设计说明了设计在准确性，资源利用率，处理时间和功耗方面的权衡。结果证明了在物联网环境中真正“永远在线”的随身摄像机系统的可能性。

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