This paper presents an approach to automatically annotate automotive radar data with AI-segmented aerial camera images. For this, the images of an unmanned aerial vehicle (UAV) above a radar vehicle are panoptically segmented and mapped in the ground plane onto the radar images. The detected instances and segments in the camera image can then be applied directly as labels for the radar data. Owing to the advantageous bird's eye position, the UAV camera does not suffer from optical occlusion and is capable of creating annotations within the complete field of view of the radar. The effectiveness and scalability are demonstrated in measurements, where 589 pedestrians in the radar data were automatically labeled within 2 minutes.Comment: 6 page...
Advantages in the application of intelligent approaches, such as the conjunction of artificial visio...
We present a novel dataset captured from a BMW X5 test carrier within the German research project KI...
We present an approach to automatically generate semantic labels for real recordings of automotive r...
This paper describes how advanced deep learning based computer vision algorithms are applied to enab...
High resolution automotive radar sensors are required in order to meet the high bar of autonomous ve...
A growing interest in technologies for autonomous driving emphasizes the demand for safe and reliabl...
The RadarScenes data set (“data set”) contains recordings from four automotive radar sensors, which ...
Image segmentation and classification of surfaces and obstacles in automotive radar imagery are the ...
This research focuses on the application of Machine Vision (MV) techniques and algorithms to the pro...
Autonomous driving requires a detailed understanding of complex driving scenes. The redundancy and c...
Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent o...
Over the last few years, the concept of civil Unmanned Aircraft System(s) (UAS) has been realised, w...
Deep Learning-based object detectors enhance the capabilities of remote sensing platforms, such as U...
This paper considers a model of the neural network for semantically segmenting the images of monitor...
This paper considers a model of the neural network for semantically segmenting the images of monitor...
Advantages in the application of intelligent approaches, such as the conjunction of artificial visio...
We present a novel dataset captured from a BMW X5 test carrier within the German research project KI...
We present an approach to automatically generate semantic labels for real recordings of automotive r...
This paper describes how advanced deep learning based computer vision algorithms are applied to enab...
High resolution automotive radar sensors are required in order to meet the high bar of autonomous ve...
A growing interest in technologies for autonomous driving emphasizes the demand for safe and reliabl...
The RadarScenes data set (“data set”) contains recordings from four automotive radar sensors, which ...
Image segmentation and classification of surfaces and obstacles in automotive radar imagery are the ...
This research focuses on the application of Machine Vision (MV) techniques and algorithms to the pro...
Autonomous driving requires a detailed understanding of complex driving scenes. The redundancy and c...
Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent o...
Over the last few years, the concept of civil Unmanned Aircraft System(s) (UAS) has been realised, w...
Deep Learning-based object detectors enhance the capabilities of remote sensing platforms, such as U...
This paper considers a model of the neural network for semantically segmenting the images of monitor...
This paper considers a model of the neural network for semantically segmenting the images of monitor...
Advantages in the application of intelligent approaches, such as the conjunction of artificial visio...
We present a novel dataset captured from a BMW X5 test carrier within the German research project KI...
We present an approach to automatically generate semantic labels for real recordings of automotive r...