ZeroSCD: Zero-Shot Street Scene Change Detection

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 to precisely delineate the boundaries of the detected changes. Extensive experiments on benchmark datasets demonstrate that ZeroSCD outperforms several state-of-the-art methods in change detection accuracy, despite not being trained on any of the benchmark datasets, proving its effectiveness and adaptability across different scenarios.