People Flow Detection Algorithm Based on a Multiencoder-Classifier Cotraining Architecture for FMCW Radar

Deep learning (DL) frameworks are widely used in various applications due to their superiority over conventional handcrafted feature-based algorithms. However, applying DL to time-range feature map-based people flow detection (PFD) with frequency-modulated continuous wave (FMCW) radar still faces several challenges: 1) simultaneously achieving people counting and motion direction recognition requires a unified framework; 2) existing mainstream network backbones designed for semantic information-rich optical images or natural language suffer performance loss in time-range feature maps with weak semantic information; and 3) the construction of labeled datasets in PFD scenes is usually costly, and limited data lead to performance loss due to overfitting. Therefore, this article proposes novel solutions from various aspects to efficiently apply DL to time-range feature map-based PFD. First, new preprocessing pipelines with Doppler spectrum analysis-based feature map truncation (DSAFMT) are proposed for the first time to simultaneously achieve people counting and direction recognition using a single radar in the radar PFD field. Second, a novel lightweight multiscale feature space fusion-based convolutional neural network backbone (MFSNet) is designed to efficiently extract multichannel differentiated representative features from time-range feature maps. Finally, a multiencoder-classifier cotraining architecture based on embedded features with two data synthesis methods and a newly designed loss function is proposed to improve the generalization ability of the algorithm. Using the test dataset collected from real scenes, the performance comparison results show that the proposed PFD algorithm outperforms the state-of-the-art algorithms and ablation studies demonstrate the effectiveness of each component of the proposed algorithm in PFD.