ZeroSCD: Zero-Shot Street Scene Change Detection
Shyam Sundar Kannan, Byung-Cheol Min

TL;DR
ZeroSCD introduces a zero-shot change detection method that uses pre-trained models for place recognition and segmentation, eliminating the need for training data and outperforming existing methods in accuracy.
Contribution
It presents a novel zero-shot framework for scene change detection that leverages existing models without training on specific datasets.
Findings
Outperforms state-of-the-art methods in accuracy
Does not require training data
Effective across diverse scenarios
Abstract
Scene Change Detection is a challenging task in computer vision and robotics that aims to identify differences between two images of the same scene captured at different times. Traditional change detection methods rely on training models that take these image pairs as input and estimate the changes, which requires large amounts of annotated data, a costly and time-consuming process. To overcome this, we propose ZeroSCD, a zero-shot scene change detection framework that eliminates the need for training. ZeroSCD leverages pre-existing models for place recognition and semantic segmentation, utilizing their features and outputs to perform change detection. In this framework, features extracted from the place recognition model are used to estimate correspondences and detect changes between the two images. These are then combined with segmentation results from the semantic segmentation model…
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Taxonomy
TopicsAutomated Road and Building Extraction · Video Surveillance and Tracking Methods · Remote-Sensing Image Classification
