Where to drive: free space detection with one fisheye camera
This addresses a data scarcity problem for researchers and developers in autonomous driving using fisheye cameras, but it is incremental as it applies existing synthetic data methods to a new camera type.
The paper tackled the lack of labeled training data for omnidirectional fisheye cameras in autonomous driving by proposing synthetic training data generated using Unity3D and a five-pass algorithm, and found that this synthetic data can be effectively used for free space detection with deep learning networks.
The development in the field of autonomous driving goes hand in hand with ever new developments in the field of image processing and machine learning methods. In order to fully exploit the advantages of deep learning, it is necessary to have sufficient labeled training data available. This is especially not the case for omnidirectional fisheye cameras. As a solution, we propose in this paper to use synthetic training data based on Unity3D. A five-pass algorithm is used to create a virtual fisheye camera. This synthetic training data is evaluated for the application of free space detection for different deep learning network architectures. The results indicate that synthetic fisheye images can be used in deep learning context.