Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints
This addresses change detection for moving cameras in applications like ground and aerial vehicles, where traditional methods struggle due to viewpoint differences, representing an incremental improvement over existing techniques.
The paper tackles the problem of detecting scene changes from image pairs with differing camera viewpoints, particularly for moving cameras where pose estimation is difficult, by proposing a dense optical flow based convolutional neural network. The method outperforms state-of-the-art algorithms on a panoramic change detection dataset.
This paper presents a novel method for detecting scene changes from a pair of images with a difference of camera viewpoints using a dense optical flow based change detection network. In the case that camera poses of input images are fixed or known, such as with surveillance and satellite cameras, the pixel correspondence between the images captured at different times can be known. Hence, it is possible to comparatively accurately detect scene changes between the images by modeling the appearance of the scene. On the other hand, in case of cameras mounted on a moving object, such as ground and aerial vehicles, we must consider the spatial correspondence between the images captured at different times. However, it can be difficult to accurately estimate the camera pose or 3D model of a scene, owing to the scene changes or lack of imagery. To solve this problem, we propose a change detection convolutional neural network utilizing dense optical flow between input images to improve the robustness to the difference between camera viewpoints. Our evaluation based on the panoramic change detection dataset shows that the proposed method outperforms state-of-the-art change detection algorithms.