Leveraging Object Proposals for Object-Level Change Detection

Feature-based image differencing is an efficient approach to image change detection, which performs fast enough for self-driving car and robotic applications. Extant approaches typically take local keypoint features as input to the differencing stage. In this study, we aim to extend the differencing stage to consider object-level features. Our object level approach is inspired by recent advances in two independent object-region proposal techniques: supervised object proposal (e.g., YOLO) and unsupervised object proposal (e.g., BING). A difficulty arises from the fact that even state-of-the-art object proposal techniques suffer from misdetections and false alarms. Our key concept is combining the supervised and unsupervised techniques into a common framework that evaluates the likelihood of change at the semantic object level. We address a challenging urban scenario using the publicly available Malaga dataset and experimentally verify that improved change detection performance can be obtained with our approach.