Abstract
Abstract. Vehicle environment cameras observing traffic participants in the area around a car and interior cameras observing the car driver are important data sources for driver intention recognition algorithms. To combine information from both camera groups, a camera system calibration can be performed. Typically, there is no overlapping field-of-view between environment and interior cameras. Often no marked reference points are available in environments, which are a large enough to cover a car for the system calibration. In this contribution, a calibration method for a vehicle camera system with non-overlapping camera groups in an urban environment is described. A-priori images of an urban calibration environment taken with an external camera are processed with the structure-frommotion method to obtain an environment point cloud. Images of the vehicle interior, taken also with an external camera, are processed to obtain an interior point cloud. Both point clouds are tied to each other with images of both image sets showing the same real-world objects. The point clouds are transformed into a self-defined vehicle coordinate system describing the vehicle movement. On demand, videos can be recorded with the vehicle cameras in a calibration drive. Poses of vehicle environment cameras and interior cameras are estimated separately using ground control points from the respective point cloud. All poses of a vehicle camera estimated for different video frames are optimized in a bundle adjustment. In an experiment, a point cloud is created from images of an underground car park, as well as a point cloud of the interior of a Volkswagen test car is created. Videos of two environment and one interior cameras are recorded. Results show, that the vehicle camera poses are estimated successfully especially when the car is not moving. Position standard deviations in the centimeter range can be achieved for all vehicle cameras. Relative distances between the vehicle cameras deviate between one and ten centimeters from tachymeter reference measurements.
Highlights
One of the big goals in the automotive industry is to reduce the number of traffic fatalities to zero (Volvo Vision 2020 (Samuelsson, 2017))
The same way, images of the vehicle interior cameras are matched with interior images and the 3d points of the interior point cloud used for pose estimation of the vehicle interior cameras in the vehicle coordinate system
The method can be used for vehicle environment and vehicle interior cameras with no overlapping fields-of-view
Summary
One of the big goals in the automotive industry is to reduce the number of traffic fatalities to zero (Volvo Vision 2020 (Samuelsson, 2017)). To anticipate the intention of the own car driver, interior cameras observing his behavior (figure 1) can be used in addition Their images can be used to extract features about the driver’s head movement and his gaze direction. As costs are a very important factor in the automotive industry, the number of cameras is kept small This leads in addition to non-overlapping fields-of-view, making the camera system calibration using tie points in overlapping image parts impossible. Due to their wide field-of-view and their low costs, so called “action cameras” can cover a huge part of the environment around a car despite their small number. The method in the cited paper requires, that each camera can see the 3d reference points, which might not be true for vehicle interior cameras
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