Research Article
Deep Learning Based Target Activity Recognition Using FMCW Radar
@INPROCEEDINGS{10.1007/978-3-030-69066-3_42, author={Bo Li and Xiaotian Yu and Fan Li and Qiming Guo}, title={Deep Learning Based Target Activity Recognition Using FMCW Radar}, proceedings={Artificial Intelligence for Communications and Networks. Second EAI International Conference, AICON 2020, Virtual Event, December 19-20, 2020, Proceedings}, proceedings_a={AICON}, year={2021}, month={7}, keywords={Deep learning Activity recognition FMCW}, doi={10.1007/978-3-030-69066-3_42} }- Bo Li
Xiaotian Yu
Fan Li
Qiming Guo
Year: 2021
Deep Learning Based Target Activity Recognition Using FMCW Radar
AICON
Springer
DOI: 10.1007/978-3-030-69066-3_42
Abstract
Target activity recognition has many potential applications in the fields of human-computer interaction, smart environment, smart system, Recent years, due to the miniaturized design of the frequency modulated continuous wave (FMCW) radar, it has been widely utilized to realize target activity recognition in our daily life. However, the activity recognition accuracy is usually not high due to the surrounding noise and variation of the activity. To realize high accuracy activity recognition, one feasible way is to extract discriminative features from the weak radar signals reflected by the activity. Inspired by the successful application of deep learning in computer vision, in this paper, we try to explore leveraging deep learning to solve the target activity recognition task. Specifically, based on the characteristics of the FMCW signals, we design the Doppler radio images suitable for the deep network to deal with. Then, we develop a deep convolutional network to extract discriminative activity features from the Doppler radio images. Finally, we feed the features into a Softmax classifier to recognize the activity. We carry out extensive experiments on a 77 GHz FMCW radar testbed. The experimental results show the excellent target activity recognition performance.