Abstract
Over the past decades, Human action recognition is very significant for human action analysis, which is a strongly active research area. The relevant target detection and classification technologies have become research hot topics, with a very wide range of applications, including security protection, disaster relief, smart home and other fields. In the academia, radar has gradually become the focus of research in the field of target detection and classification. Compared with the other sensors, radar is more advanced in non-environmental impact and good data integrity in human body detection and classification. While the current studies have combined machine learning with radar signals, the performance of machine learning methods such as SVM and decision tree is not well. To address this issue, this work utilized convolutional neural networks, combined with micro-Doppler image features extracted from radar signals. The experimental demonstrated that CNN model achieved a higher accuracy in human activity recognition task.
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Zhang, H. (2022). Radar-Based Activity Recognition with Deep Learning Model. In: Hung, J.C., Yen, N.Y., Chang, JW. (eds) Frontier Computing. FC 2021. Lecture Notes in Electrical Engineering, vol 827. Springer, Singapore. https://doi.org/10.1007/978-981-16-8052-6_42
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DOI: https://doi.org/10.1007/978-981-16-8052-6_42
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