Self-supervised Simultaneous Alignment and Change Detection

This study proposes a self-supervised method for detecting scene changes from an image pair. For mobile cameras such as drive recorders, to alleviate the camera viewpoints' difference, image alignment and change detection must be optimized simultaneously because they depend on each other. Moreover, lighting condition makes the scene change detection more difficult because it widely varies in images taken at different times. To solve these challenges, we propose a self-supervised simultaneous alignment and change detection network (SACD-Net). The proposed network is robust specifically in differences of camera viewpoints and lighting conditions to simultaneously estimate warping parameters and multi-scale change probability maps while change regions are not taken into account of calculation of the feature consistency and semantic losses. Based on comparative analysis between our self-supervised and the previous supervised models as well as ablation study of the losses of SACD-Net, the results show the effectiveness of the proposed method using a synthetic dataset and our new real dataset.