Quickest Detection of a Moving Target in a Sensor Network

Author(s):  
Georgios Rovatsos ◽  
Shaofeng Zou ◽  
Venugopal V. Veeravalli
2014 ◽  
Vol 556-562 ◽  
pp. 3101-3106
Author(s):  
Wen Zhen Zhou ◽  
Zhi Zhuang Liu

Aiming at cluster control methods existing network fragmentation, formations loss and poor tracking performance problems in mobile sensor network target tracking, a novel adaptive cluster control method is proposed to track moving target in a dynamic environment. In the proposed method, each agent gets parameters of the network in a decentralized way in order to determine the range of the network. Therefore, connectivity of the network, formation and tracking performance can be improved when it meets obstacles. In addition, in order to verify the effectiveness of the proposed method, some experiments have been performed to compare the proposed method with other existing methods. And experiments have demonstrated the effectiveness of the proposed method.


Sensors ◽  
2009 ◽  
Vol 9 (5) ◽  
pp. 3563-3585 ◽  
Author(s):  
Kazuya Tsukamoto ◽  
Hirofumi Ueda ◽  
Hitomi Tamura ◽  
Kenji Kawahara ◽  
Yuji Oie

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Qichang Xu

Aiming at the shortcomings of traditional moving target detection methods in complex scenes such as low detection accuracy and high complexity, and not considering the overall structure information of the video frame image, this paper proposes a moving-target detection based on sensor network. First, a low-power motion detection wireless sensor network node is designed to obtain motion detection information in real time. Secondly, the background of the video scene is quickly extracted by the time domain averaging method, and the video sequence and the background image are channel-merged to construct a deep full convolutional network model. Finally, the network model is used to learn the deep features of the video scene and output the pixel-level classification results to achieve moving target detection. This method not only can adapt to complex video scenes of different sizes but also has a simple background extraction method, which effectively improves the detection speed.


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