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基于卷積神經網絡的雷達人體動作與身份多任務識別

Human Activity and IdentityMulti-Task Recognition Based on Convolutional Neural Network Using Doppler Radar

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摘要

為彌補單任務識別未充分利用相關任務監督信息的缺陷,提出了一種基于卷積神經網絡的多任務識別模型。該模型引入注意力機制,對任務共享層的特征進行重校正,并結合多尺度結構進行特征融合,最后在任務特定層上進行多任務識別。針對共享特征空間內類分布不緊湊導致的模型泛化性能降低問題,本文在模型中引入中心損失函數與均方誤差損失函數,與傳統的交叉熵損失函數相結合,共同優化模型。實驗結果表明:所提模型在人體6個動作類別和15個身份類別上的最高識別準確率分別可達100%和99.93%,兩種任務上識別的總準確率可達99.93%,均優于任務獨立識別時的各項準確率,說明所提模型能更有效地同時完成人體動作及身份識別任務。

Abstract

A multitask recognition model based on convolutional neural network is proposed to avoid single task recognition ignoring supervision information of related tasks. The proposed model introduces an attention mechanism to perform feature recalibration of the task shared layer and combines the multiscale structure for feature fusion. Finally, multi-task recognition is performed on the task-specific layers. Center loss and mean square error loss functions are employed together with the traditional cross entropy loss function to solve the generalization degradation problem caused by uncompact class distribution in the shared feature space. Experimental results on 6 human activities and 15 identities show that the model can achieve the maximum recognition accuracies of 100% and 99.93% on each task, respectively, and the multitask accuracy is up to 99.93%. The results are better than those obtained by the single task models. This shows that the model can simultaneously perform human activity and identity recognition more effectively.

Newport宣傳-MKS新實驗室計劃
補充資料

中圖分類號:TP391.4

DOI:10.3788/LOP57.021009

所屬欄目:圖像處理

基金項目:國家自然科學基金;

收稿日期:2019-06-10

修改稿日期:2019-07-01

網絡出版日期:2020-01-01

作者單位    點擊查看

侯春萍:天津大學電氣自動化與信息工程學院, 天津 300072
蔣天麗:天津大學電氣自動化與信息工程學院, 天津 300072
郎玥:天津大學電氣自動化與信息工程學院, 天津 300072
楊陽:天津大學電氣自動化與信息工程學院, 天津 300072

聯系人作者:郎玥(langyue@tju.edu.cn)

備注:國家自然科學基金;

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引用該論文

Hou Chunping,Jiang Tianli,Lang Yue,Yang Yang. Human Activity and IdentityMulti-Task Recognition Based on Convolutional Neural Network Using Doppler Radar[J]. Laser & Optoelectronics Progress, 2020, 57(2): 021009

侯春萍,蔣天麗,郎玥,楊陽. 基于卷積神經網絡的雷達人體動作與身份多任務識別[J]. 激光與光電子學進展, 2020, 57(2): 021009

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