围栏的人类活动识别优化算法

doi:10.19665/j.issn1001-2400.2019.01.015

Abstract

Abstract:

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%.

Key words: inertial sensor, fence, human activity recognition, neural network

CLC Number:

TP391

HU Kelu,WANG Yingguan. Fence human activity recognition optimized algorithm[J].Journal of Xidian University, 2019, 46(1): 93-97.

Figures/Tables 5

References 18

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活动类型	基准算法		优化算法
活动类型	准确率	召回率	准确率	召回率
踢	91.1	94.0	94.0	95.6
拍	91.1	90.0	95.4	94.6
靠	93.9	91.6	95.3	94.6
无	97.0	97.5	100.0	100.0

Fence human activity recognition optimized algorithm

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