视觉任务中能源资源合理开发的智能摄像机网络监控

M. Bhargavi, Syed Shareefunnisa
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引用次数: 0

摘要

智能摄像头网络在静态模式和动态模式下的人脸检测是通过多任务分配来完成的。这可能会导致部分行人的缺失和单个目标消耗更多的能量。本文主要介绍了应用最广泛的动态模式下的能量优化技术。任务是定期调度的,目标切换和任务切换的时间不够。视觉任务中的人脸检测是利用Viola Jones算法中的Haar和AdaBoost的矩形特征来完成的。在最大任务优先算法的基础上分配能量效用因子。将任务分配给能够更接近地检测人脸的利用率最高的摄像机。通过基于分布式市场的竞价过程和自适应策略选择对能源消耗进行优化。这提高了相机的存在性,并在限制相机数量的情况下降低了功耗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Smart Camera Network Supervision for Competent Exploitation of Energy Recourses in vision Task
Face Detection by Smart Camera Network in static mode and dynamic mode is done by the allocation of multitasking. This might result in the absence of some of the pedestrians and consumption of more energy for single target. This paper mainly presents most widely used energy optimization technique in dynamic mode. Tasks are scheduled on periodic basis, with inadequate time period for target switching and task switching. Face Detection in vision task is done by rectangular features of Haar and AdaBoost in Viola Jones Algorithm. Energy utility factor is allocated on Largest Task first algorithm basis. Task is allocated to the camera with uppermost amount of utility rate that can detect the face more approximately. Energy Consumption is optimized by the distributive market based bidding process and Adaptive strategy selection. This boosts existence of camera and drop in the power consumption with restricted amount of Camera's.
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