3D Detection of Vehicles from 2D Images in Traffic Surveillance

Traffic surveillance systems use monocular cameras and automatic visual algorithms to locate and observe traffic movement. Object detection results in 2D object boxes around vehicles, which relate to inaccurate real-world locations. In this paper, we employ the existing KM3D CNN-based 3D detection model, which directly estimates 3D boxes around vehicles in the camera image. However, the KM3D model has only been applied earlier in autonomous driving use cases with different camera viewpoints. However, 3D annotation datasets are not available for traffic surveillance, requiring the construction of a new dataset for training the 3D detector. We propose and validate four different annotation configurations that generate 3D box annotations using only camera calibration, scene information (static vanishing points) and existing 2D annotations. Our novel Simple box method does not require segmentation of vehicles and provides a more simple 3D box construction, which assumes a fixed predefined vehicle width. The Simple box pipeline provides the best 3D object detection results, resulting in 51.9% AP3D using KM3D trained on this data. The 3D object detector can estimate an accurate 3D box up to a distance of 125 meters from the camera, with a median middle point mean error of only 0.5-1.0 meter.