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- W4379116342 abstract "Aiming to solve the issues of high data dimensions with Frequency Modulated Continuous Wave (FMCW) radar image, slow extraction of feature information and complex classifiers in recognition algorithms, this paper propose a human activity recognition algorithm using two-dimensional feature extraction for FMCW radar. First, two-dimensional principal component analysis (2DPCA) is used to reduce the dimension of the radar Doppler-Time Map (DTM). On this basis, two-dimensional linear discriminant analysis (2DLDA) is used to extract the category feature information. Finally, K-Nearest Neighbor (KNN)classifier is used to achieve human activity recognition. The method proposed is verified by using the open dataset “Radar signatures of human activities” created by the University of Glasgow. The human activity recognition rate reaches 96.40%. The results show that compared with the existing feature extraction algorithm in this field, the new method can effectively extract the key feature information of radar images and improve the recognition accuracy of human activity, meanwhile the running time is shortened." @default.
- W4379116342 created "2023-06-03" @default.
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- W4379116342 date "2023-01-06" @default.
- W4379116342 modified "2023-10-16" @default.
- W4379116342 title "Radar-based human activity recognition using two-dimensional feature extraction" @default.
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- W4379116342 doi "https://doi.org/10.1109/iccece58074.2023.10135278" @default.
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