Weakly Supervised Silhouette-based Semantic Scene Change Detection
arXiv:1811.11985
Abstract
This paper presents a novel semantic scene change detection scheme with only weak supervision. A straightforward approach for this task is to train a semantic change detection network directly from a large-scale dataset in an end-to-end manner. However, a specific dataset for this task, which is usually labor-intensive and time-consuming, becomes indispensable. To avoid this problem, we propose to train this kind of network from existing datasets by dividing this task into change detection and semantic extraction. On the other hand, the difference in camera viewpoints, for example, images of the same scene captured from a vehicle-mounted camera at different time points, usually brings a challenge to the change detection task. To address this challenge, we propose a new siamese network structure with the introduction of correlation layer. In addition, we collect and annotate a publicly available dataset for semantic change detection to evaluate the proposed method. The experimental results verified both the robustness to viewpoint difference in change detection task and the effectiveness for semantic change detection of the proposed networks. Our code and dataset are available at https://kensakurada.github.io/pscd.
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Joint 2D-3D-Semantic Data for Indoor Scene Understanding
- Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
- Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints
- Multitask Learning for Large-scale Semantic Change Detection
- M4CD: A Robust Change Detection Method for Intelligent Visual Surveillance