Adapting CNNs for Fisheye Cameras without Retraining
Ryan Griffiths Donald G. Dansereau
The majority of image processing approaches assume images are in or can be
rectified to a perspective projection. However, in many applications it is
beneficial to use non conventional cameras, such as fisheye cameras, that have
a larger field of view (FOV). The issue arises that these large-FOV images
can't be rectified to a perspective projection without significant cropping of
the original image. To address this issue we propose Rectified Convolutions
(RectConv); a new approach for adapting pre-trained convolutional networks to
operate with new non-perspective images, without any retraining. Replacing the
convolutional layers of the network with RectConv layers allows the network to
see both rectified patches and the entire FOV. We demonstrate RectConv adapting
multiple pre-trained networks to perform segmentation and detection on fisheye
imagery from two publicly available datasets. Our approach requires no
additional data or training, and operates directly on the native image as
captured from the camera. We believe this work is a step toward adapting the
vast resources available for perspective images to operate across a broad range
of camera geometries.