TWReID: Through-Wall and Free-Walking Person Re-Identification Based on MIMO Radar

Person re-identification (Re-ID), as a core technology for target tracking and identity authentication, is widely applied in intelligent surveillance and security systems. In recent years, radio frequency sensing-based person Re-ID has attracted significant attention due to its robustness against visual interference. However, existing research mainly focuses on unobstructed and fixed-route scenarios, which restrains its applications in real world. In this work, we propose TWReID, a through-wall and free-walking person Re-ID system based on our customized multiple-input multiple-output (MIMO) radar with 4 transmitting (TX) $\times 16$ receiving (RX) antenna array, which produces $1\sim 2$ GHz frequency-modulated continuous wave (FMCW) signals to penetrate walls and reflect human signals. During the stage of data pre-processing, we design an additional input branch generated by Doppler-static background subtraction (DSBS) to prevent the removal of individuals with minor movements in moving average background subtraction (MABS). Then the raw polar coordinate data is converted into a world Cartesian coordinate system to mitigate the viewpoint dependence. To further characterize the gait information, TWReID presents a dual-branch feature extraction network termed statistic contextual multi-scale channel-aware network (SCMCNet), in which multi-scale channel-aware spatio-temporal attention (MCSA) is constructed to achieve adaptive spatial-temporal feature fusion. Experiments show that TWReID achieves mAP and CMC-1 of 86.8% and 96.5% on a dataset of 14 people walking freely, and mAP and CMC-1 of 64.6% and 95% on a dataset containing different behaviors, outperforming existing identity recognition and person Re-ID methods.