Abstract
We propose to find changes in the constantly changing world, given visual observations at street-level. In particular, we identify “longterm” changes between Google Street View images and dashcam videos captured at different months or even years. This is a challenging task, since (1) dashcam frames are not localized in world coordinate, and (2) there are many changes introduced by moving objects. We propose a robust sequence alignment method to align dashcam sequence to Street View images. Our method outperforms a strong baseline method [1] by 12% mean Average Precision (AP). We also propose a novel change detection method designed to detect long-term changes. Our change detection method (13.54%) outperforms a baseline method without handling car interior and moving objects (11.70%) by 1.84% (relatively 13.6%) in mean AP. In a controlled experiment, given manually aligned high quality Street View images, our change detection method achieves a significantly better mean AP (45.57%).