Change Detection with Global Viewpoint Localization

Abstract

This paper addresses the problem of change detection from a novel perspective of long-term map learning. We are particularly interested in designing an approach that can scale to large maps and that can function under global uncertainty in the viewpoint (i.e., GPS-denied situations). Our approach, which utilizes a compact bag-of-words (BoW) scene model, makes several contributions to the problem: 1) Two kinds of prior information are extracted from the view sequence map and used for change detection. Further, we propose a novel type of prior, called motion prior, to predict the relative motions of stationary objects and anomaly ego-motion detection. The proposed prior is also useful for distinguishing stationary from non-stationary objects. 2) A small set of good reference images (e.g., 10) are efficiently retrieved from the view sequence map by employing the recently developed Bag-of-Local-Convolutional-Features (BoLCF) scene model. 3) Change detection is reformulated as a scene retrieval over these reference images to find changed objects using a novel spatial Bag-of-Words (SBoW) scene model. Evaluations conducted of individual techniques and also their combinations on a challenging dataset of highly dynamic scenes in the publicly available Malaga dataset verify their efficacy.

Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges

“…In addition to the neural network in AI, there are other AI techniques used for implementing change detection. Recently, dictionary learning has been employed, and it focuses on learning internal feature representations from datasets [141,176,224,225], just like AEs. The cellular automata (CA), a spatially and temporally discrete model inspired by cellular behavior, can help to model future changes in LULC [226] and predict urban spatial expansion [227].…”