Environmental Change Detection for Real-World Change Analysis

Scene Change Detection (SCD) evaluates changes using pre-defined query-reference (i.e., present-past) image pairs. However, thisformulation overlooks a critical dependency: the corresponding query-reference pair is assumed to be prepared in advance. In real-world appli-cations, such as mobile robots, future query views cannot be known inadvance, and thus their corresponding reference images cannot be pre-defined. To remove this dependency and push change detection towardmore practical applications, we introduce Environmental Change Detec-tion (ECD). A key aspect of ECD is to avoid unrealistically predefinedand aligned query-reference pairs and instead retrieve environmental cuesfrom an uncurated image database of reference scenes. To tackle thisnew challenging task, we additionally introduce an initial solution thatenables change detection under unknown and imperfect query-referenceconditions. The main idea of our solution is to retrieve multiple referencecandidates and aggregate semantically rich representations for changedetection. We further construct ECD benchmark sets by reformulatingthree standard change detection datasets. Extensive experimental resultsdemonstrate the efficacy of our solution in both ECD and SCD.