Multiple Walking People Classification with Convolutional Neural Networks Based on Micro-Doppler

Sun, Zhiyong; Wang, Jun; Peng, Lei; Qin, Zhaotao

doi:10.1109/wcsp.2018.8555912

Cited by 1 publication

(1 citation statement)

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“…Then, datasets HSLT 4D and δ HTLS are used to predict the restored HSHT dataset HTHS 4D ∈ R T C ×W×H×B . Because some details of the extracted features are aborted when passing pooling layers [20], we choose not to use pooling layers and simply use the simple structure of fully convolutional layers.…”

Section: D Cnns For 4d Residual Seriesmentioning

confidence: 99%

A Fast Three-Dimensional Convolutional Neural Network-Based Spatiotemporal Fusion Method (STF3DCNN) Using a Spatial-Temporal-Spectral Dataset

et al. 2020

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With the growing development of remote sensors, huge volumes of remote sensing data are being utilized in related applications, bringing new challenges to the efficiency and capability of processing huge datasets. Spatiotemporal remote sensing data fusion can restore high spatial and high temporal resolution remote sensing data from multiple remote sensing datasets. However, the current methods require long computing times and are of low efficiency, especially the newly proposed deep learning-based methods. Here, we propose a fast three-dimensional convolutional neural network-based spatiotemporal fusion method (STF3DCNN) using a spatial-temporal-spectral dataset. This method is able to fuse low-spatial high-temporal resolution data (HTLS) and high-spatial low-temporal resolution data (HSLT) in a four-dimensional spatial-temporal-spectral dataset with increasing efficiency, while simultaneously ensuring accuracy. The method was tested using three datasets, and discussions of the network parameters were conducted. In addition, this method was compared with commonly used spatiotemporal fusion methods to verify our conclusion.

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Section: D Cnns For 4d Residual Seriesmentioning

confidence: 99%