Perception algorithms in 3D space are key research contents in the fields of autonomous vehicles and robots. 3D perception in a bird's-eye view (BEV) has attracted a lot of attention in recent years, because the BEV's representation of traffic scenes contains abundant target depth information and dimensional information, and the detection results can be directly applied to downstream tasks such as trajectory tracking, path planning, etc. Motivated by this observation, We propose a tracking module based on bird's-eye view and a target correlation method optimized for tracking tasks. The method we propose has advantages over other algorithms in performance on the Nuscenes dataset and the KITTI dataset. We further demonstrate that by projecting 3D objects under the bird's-eye view, we can achieve significant improvements in monocular 3D perception tasks, including detection and tracking.