The capability of human identification in specific scenarios and in a quickly and accurately manner, is a critical aspect in various surveillance applications. In particular, in this context, classical surveillance systems are based on video cameras, requiring high computational/storing resources, which are very sensitive to light and weather conditions. In this paper, an efficient classifier based on deep learning is used for the purpose of identifying individuals features by resorting to the micro-Doppler data extracted from low-power frequency-modulated continuous-wave radar measurements. Results obtained through the application of a deep temporal convolutional neural networks confirms the applicability of deep learning to the problem at hand. Best obtained identification accuracy is 0.949 with an F-measure of 0.88 using a temporal window of four second.
|Number of pages||5|
|Publication status||Published - 5 Jul 2020|
|Event||IEEE International Workshop on Metrology for Aerospace 2020 - |
Duration: 22 Jun 2020 → 24 Jun 2020
|Conference||IEEE International Workshop on Metrology for Aerospace 2020|
|Period||22/06/20 → 24/06/20|
- deep learning
- gait recognition
- low-power radar
- micro doppler
Addabbo, P., Bernardi, M. L., Biondi, F., Cimitile, M., Clemente, C., & Orlando, D. (2020). Gait recognition using FMCW radar and temporal convolutional deep neural netowrks. Paper presented at IEEE International Workshop on Metrology for Aerospace 2020, .