  Although recent deep learning-based calibration methods can predict extrinsic
and intrinsic camera parameters from a single image, their generalization
remains limited by the number and distribution of training data samples. The
huge computational and space requirement prevents convolutional neural networks
(CNNs) from being implemented in resource-constrained environments. This
challenge motivated us to learn a CNN gradually, by training new data while
maintaining performance on previously learned data. Our approach builds upon a
CNN architecture to automatically estimate camera parameters (focal length,
pitch, and roll) using different incremental learning strategies to preserve
knowledge when updating the network for new data distributions. Precisely, we
adapt four common incremental learning, namely: LwF , iCaRL, LU CIR, and BiC by
modifying their loss functions to our regression problem. We evaluate on two
datasets containing 299008 indoor and outdoor images. Experiment results were
significant and indicated which method was better for the camera calibration
estimation.

Computer Vision and Pattern Recognition