mmReID: Person Reidentification Based on Commodity Millimeter-Wave Radar

Person reidentification (Re-ID) plays an increasingly important role in the development of smart cities and public security systems. Typical person Re-ID is generally used to query and retrieve pedestrians across cameras in the form of images or videos. For the sake of privacy protection and invariability to resolution, light, and occlusion, Re-ID performs excellent prospect by using millimeter-wave radars. Existing radio frequency (RF) person Re-ID approaches either suffer from relatively unreliable accuracy due to the sparse characteristics of point clouds or additionally rely on other sensing task (e.g., 3-D skeleton prediction) to avoid overfitting. In this article, we present mmReID, an RF person Re-ID system which integrates frequency-modulated continuous wave (FMCW) mmWave radar time-velocity micro-Doppler imaging heatmaps with different frequencies into the proposed dual-stream multilayer feature fusion network named ConvSnet. ConvSnet effectively fuses shallow and deep features at different levels, and adopts the attention module with intramodal aggregation to extract the contextual relevance of velocity features. In addition, we construct and publish a dataset mmReIData based on mmWave radar for person Re-ID, consisting of sampling RF data from 41 pedestrians. Experimental results show that our proposed ConvSnet network achieves the best performance against other state-of-the-art networks both in person identification and Re-ID tasks. Further ablation studies indicate the effectiveness of each component of the ConvSnet network.