  Vehicle 3D extents and trajectories are critical cues for predicting the
future location of vehicles and planning future agent ego-motion based on those
predictions. In this paper, we propose a novel online framework for 3D vehicle
detection and tracking from monocular videos. The framework can not only
associate detections of vehicles in motion over time, but also estimate their
complete 3D bounding box information from a sequence of 2D images captured on a
moving platform. Our method leverages 3D box depth-ordering matching for robust
instance association and utilizes 3D trajectory prediction for
re-identification of occluded vehicles. We also design a motion learning module
based on an LSTM for more accurate long-term motion extrapolation. Our
experiments on simulation, KITTI, and Argoverse datasets show that our 3D
tracking pipeline offers robust data association and tracking. On Argoverse,
our image-based method is significantly better for tracking 3D vehicles within
30 meters than the LiDAR-centric baseline methods.
