Selecting change image for efficient change detection

Change detection (CD) is a fundamental problem that aims at detecting changed objects from two observations. Previous CNN-based CD methods detect changes through multi-scale deep convolutional features extracted from two images. However, we find that change always occurs in the 'Query' image for fixed cameras. This condition means that changes can be detected in advance from a single image with a coarse change. In this paper, we propose an efficient CD method to detect precise changes from the change image. First, a change image selector is designed to identify the image containing changes. Second, a coarse change prior map generator is proposed to generate coarse change prior to indicate the position of changes. Then, we introduce a simple multi-scale CD module to refine the coarse change detection. As only one image is used in the multi-scale CD module, our method is more efficient in training and testing than other compared methods. Numerous experiments have been conducted to analyse the effectiveness of the proposed method. Experimental results show that the proposed method achieves superior detection performance and higher speed than other compared CD methods.