A Bi-Direction People Counting Method Based on Multiscale Clustering and Multiple Extended Target Tracking Using MIMO Radar

People counting using radar is a popular technology because of its robustness to darkness, sensitivity to human movement, noncontact wireless sensing, and no privacy issues. However, tracking and counting multiple people by radar is still a challenge. In this article, we propose a bi-direction people counting method based on multiscale clustering and multiple extended target tracking using multiple-input–multiple-output (MIMO) radar. The core idea is to correctly obtain the trajectory of each person and use its trajectory to judge the entry and exit of people. Specifically, we first perform distance, angle, and velocity estimation to obtain a large number of people target point clouds with high resolution. Second, we develop a multiscale clustering method, including microclustering, macroclustering, and precision-clustering so as to identify people target more accurately while maintaining low computational complexity. Then, we use the two-stage data association method to associate macrocluster, and then associate point clouds in macrocluster, which improves the accuracy of data association in the tracking process. Finally, we count people according to the prior edge areas and the obtained trajectories. Experimental results show that the proposed method can effectively detect and track multiple people targets close to each other, and reach the accuracy of people counting above 97%.