Fence human activity recognition optimized algorithm

被引：0

作者：

Hu K. ^{[1
,2
]}

Wang Y. ^{[1
]}

机构：

[1] Chinese Academy of Sciences, Shanghai Institute of Microsystem and Information Technology, Shanghai

[2] University of Chinese Academy of Sciences, School of Microelectronics, Beijing

来源：

Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2019年 / 46卷 / 01期

关键词：

Fence; Human activity recognition; Inertial sensor; Neural network;

D O I：

10.19665/j.issn1001-2400.2019.01.015

中图分类号：

学科分类号：

摘要：

In order to effectively recognize the human activities on a fence, we optimize the original human activity recognition system by proposing two algorithms: multi-level classification and multi-node fusion. The multi-level classification algorithm eliminates background data without activities in first level classification, reduces the amount of transferred data, and improves the accuracy of the next level. The multi-node fusion algorithm improves the reliability of the results by merging the recognition results of the neighboring nodes. Based on the data of experimental environment, the effectiveness of the algorithm is verified. The data transfer rate and data transmission amount of the multi-level classification algorithm are much lower than those of the benchmark algorithm. The multi-node fusion algorithm removes the redundancy recognition results by up to 67.7%. © 2019, The Editorial Board of Journal of Xidian University. All right reserved.

引用

页码：93 / 97and105

共 18 条

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