The perception of Intelligent Transportation Systems is mainly based on conventional cameras. Event-based cameras have a high potential to increase detection performance in such sensor systems. Therefore, an extrinsic calibration between these sensors is required. Since a target-based method with a checkerboard on the highway is impractical, a targetless approach is necessary. To the best of our knowledge, no working approach for targetless extrinsic calibration between event-based and conventional cameras in the domain of ITS exists. To fill this knowledge gap, we provide a targetless approach for extrinsic calibration. Our algorithm finds correspondences of the detected motion between both sensors using deep learning-based instance segmentation and sparse optical flow. Then, it calculates the transformation. We were able to verify the effectiveness of our method during experiments. Furthermore, we are comparable to existing multicamera calibration methods. Our approach can be used for targetless extrinsic calibration between event-based and conventional cameras.     «

The perception of Intelligent Transportation Systems is mainly based on conventional cameras. Event-based cameras have a high potential to increase detection performance in such sensor systems. Therefore, an extrinsic calibration between these sensors is required. Since a target-based method with a checkerboard on the highway is impractical, a targetless approach is necessary. To the best of our knowledge, no working approach for targetless extrinsic calibration between event-based and conventio...     »