Florian Schuller

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 vehicles 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 systems mean localization accuracy of 0.37 m for a variety of different sequences.

Arbitrary object localization and tracking via multiple-camera surveillance system embedded in a parking garage

We illustrate a multiple-camera surveillance system installed in a parking garage to detect arbitrary moving objects. Our system is real-time capable and computes precise and reliable object positions. These objects are tracked to warn of collisions, e.g. between vehicles, pedestrians or other vehicles. The proposed system is based on multiple grayscale cameras connected by a local area network. Each camera shares its field of view with other cameras to handle occlusions and to enable multi-view vision. We aim at using already installed hardware found in many modern public parking garages. The system’s pipeline starts with the synchronized image capturing process separately for each camera. In the next step, moving objects are selected by a foreground segmentation approach. Subsequently, the foreground objects from a single camera are transformed into view rays in a common world coordinate system and are joined to receive plausible object hypotheses. This transformation requires a one-time initial intrinsic and extrinsic calibration beforehand. Afterwards, these view rays are filtered temporally to arrive at continuous object tracks. In our experiments we used a precise LIDAR-based reference system to evaluate and quantify the proposed system’s precision with a mean localization accuracy of 0.24m for different scenarios.

Park marking-based vehicle self-localization with a fisheye topview system

Accurately self-localizing a vehicle is of high importance as it allows to robustify nearly all modern driver assistance functionality, e.g., lane keeping and coordinated autonomous driving maneuvers. We examine vehicle self-localization relying only on video sensors, in particular, a system of four fisheye cameras providing a view surrounding the car, a setup currently growing popular in upper-class cars. The presented work aims at an autonomous parking scenario. The method is based on park markings as orientation marks since they can be found in nearly every parking deck and require only little additional preparation. Our contribution is twofold: (1) we present a new real-time capable image processing pipeline for topview systems extracting park markings and show how to obtain a reliable and accurate ego pose and ego motion estimation given a coarse pose as starting point. (2) The aptitude of this often neglected sensor array for vehicle self-localization is demonstrated. Experimental evaluation yields a precision of 0.15

Verfahren und ein System zum Lokalisieren einer mobilen Einrichtung

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Verfahren zur Verkehrssteuerung in einer Parkumgebung

± 0.18 m and 2.01

Verfahren zur Navigation eines Benutzers zwischen einer ersten Position innerhalb eines Gebäudebereichs und einer zweiten Position

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