异构视频传感器网络目标全视角覆盖估计模型

覆盖是评价视频传感器网络对监测区域感知质量的重要指标.与传统的覆盖问题不同,随着目标的移动,全视角覆盖需要从任意方向捕捉到目标的有效正面,其覆盖估计问题也更加复杂.目前,大量的研究工作主要假设同构节点随机部署在监测区域内以实现全视角覆盖,本文研究异构视频传感器网络全视角覆盖估计问题,为消除边界效应,提出了扩展监测区域及最大探测区域的概论,并推导出目标全视角覆盖估计模型,通过实验对场景仿真值及模型理论值进行比较分析,结果表明它们之间的平均绝对覆盖误差基本保持在6.5％以内.

Full-View Coverage Estimation in Heterogeneous Visual Sensor Networks

Coverage is an important metric for evaluating the sensing quality of visual sensor networks (VSNs).Unlike traditional coverage issues,full-view coverage requires to capture target’s effective face from any direction,and its coverage estimation issue is more complicated.At present,most of literatures assume that a great number of homogeneous visual sensors are randomly scattered in the monitoring area to achieve full-view.This paper studies the full-view estimation problem in heterogeneous VSNs.In order to eliminate the boundary effect,the concepts of extended monitoring area and maximum detection area are introduced.To evaluate the performance of the proposed full-view estimation expression,several simulation experiments are conducted to validate the theoretical results.The results show that the mean absolute coverage error between the theoretical values and the experimental values is controlled less than 6.5%.