Use of Generative Adversarial Network for Cross-Domain Change Detection
This addresses the problem of detecting changes in images across different domains (e.g., weather or time of day) for applications like remote sensing or surveillance, but it is incremental as it builds on existing GAN and change detection methods.
The paper tackles cross-domain change detection by using a GAN to translate reference images into the query domain, enabling in-domain comparison and leveraging existing change detectors. Experiments show the approach is effective, though no specific performance numbers are provided.
This paper addresses the problem of cross-domain change detection from a novel perspective of image-to-image translation. In general, change detection aims to identify interesting changes between a given query image and a reference image of the same scene taken at a different time. This problem becomes a challenging one when query and reference images involve different domains (e.g., time of the day, weather, and season) due to variations in object appearance and a limited amount of training examples. In this study, we address the above issue by leveraging a generative adversarial network (GAN). Our key concept is to use a limited amount of training data to train a GAN-based image translator that maps a reference image to a virtual image that cannot be discriminated from query domain images. This enables us to treat the cross-domain change detection task as an in-domain image comparison. This allows us to leverage the large body of literature on in-domain generic change detectors. In addition, we also consider the use of visual place recognition as a method for mining more appropriate reference images over the space of virtual images. Experiments validate efficacy of the proposed approach.