Towards highly automated driving in a parking garage: General object localization and tracking using an environment-embedded camera system

In this study, we present a new indoor positioning and environment perception system for generic objects based on multiple surveillance cameras. In order to assist highly automated driving, our system detects the vehicle's position and any object along its current path to avoid collisions. A main advantage of the proposed approach is the usage of cameras that are already installed in the majority of parking garages. We generate precise object hypotheses in 3D world coordinates based on a given extrinsic camera calibration. Starting with a background subtraction algorithm for the segmentation of each camera image, we propose a robust view-ray intersection approach that enables the system to match and triangulate segmented hypotheses from all cameras. Comparing with LIDAR-based ground truth, we were able to evaluate the system's mean localization accuracy of 0.37 m for a variety of different sequences.