  This paper proposes a method to extract the position and pose of vehicles in
the 3D world from a single traffic camera. Most previous monocular 3D vehicle
detection algorithms focused on cameras on vehicles from the perspective of a
driver, and assumed known intrinsic and extrinsic calibration. On the contrary,
this paper focuses on the same task using uncalibrated monocular traffic
cameras. We observe that the homography between the road plane and the image
plane is essential to 3D vehicle detection and the data synthesis for this
task, and the homography can be estimated without the camera intrinsics and
extrinsics. We conduct 3D vehicle detection by estimating the rotated bounding
boxes (r-boxes) in the bird's eye view (BEV) images generated from inverse
perspective mapping. We propose a new regression target called
\textit{tailed~r-box} and a \textit{dual-view} network architecture which
boosts the detection accuracy on warped BEV images. Experiments show that the
proposed method can generalize to new camera and environment setups despite not
seeing imaged from them during training.
