Detection of Camera Model Inconsistency and the Existence of Optical Image Stabilization System.

Cameras becomes more ubiquitous on mobile devices which pave the way for Augment Reality (AR) applications. AR's enabling technology is the underlying visual-inertial Simultaneous Localization and Mapping (SLAM) package which requires a precise camera model for mapping purpose. Due to manufacturing inconsistency and device aging over time, the preciseness is often hard to maintain over time. On the other hand, those cameras are often equipped with optical image stabilization (OIS) system. OIS changes camera intrinsic parameters and being aware of its existence is important before a high-order SLAM model is applied. Here we present a two-step approach to detect if an image conforms to a given camera model (distortion coefficient and intrinsic matrix) by developing two statistical hypothesis testings. We have implemented the algorithm and test it in physical experiments. Results show that our algorithm successfully detects model inconsistency and the existence of OIS system with 85.4% recall and 100% precision.