3D pose estimation by fitting image gradients directly to polyhedral models

This contribution addresses the problem of pose estimation and tracking of vehicles in image sequences from traffic scenes recorded by a stationary camera. In a new algorithm, the vehicle pose is estimated by directly fitting image gradients to polyhedral vehicle models without an edge segment extraction process. The new approach is significantly more robust than approaches that rely on feature extraction because the new approach exploits more information from the image data. We can track vehicles that are partially occluded by textured objects, e.g. foliage, where classical approaches based on edge segmentation extraction fail. Results from various experiments with real world traffic scenes are presented.