Epipolar-Guided Deep Object Matching for Scene Change Detection
arXiv:2007.15540
Abstract
This paper describes a viewpoint-robust object-based change detection network (OBJ-CDNet). Mobile cameras such as drive recorders capture images from different viewpoints each time due to differences in camera trajectory and shutter timing. However, previous methods for pixel-wise change detection are vulnerable to the viewpoint differences because they assume aligned image pairs as inputs. To cope with the difficulty, we introduce a deep graph matching network that establishes object correspondence between an image pair. The introduction enables us to detect object-wise scene changes without precise image alignment. For more accurate object matching, we propose an epipolar-guided deep graph matching network (EGMNet), which incorporates the epipolar constraint into the deep graph matching layer used in OBJCDNet. To evaluate our network's robustness against viewpoint differences, we created synthetic and real datasets for scene change detection from an image pair. The experimental results verified the effectiveness of our network.
8 pages, 4 figures
References in corpus (4)
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Learning to Measure Change: Fully Convolutional Siamese Metric Networks for Scene Change Detection
- Progressive Attention Networks for Visual Attribute Prediction
- Dense Optical Flow based Change Detection Network Robust to Difference of Camera Viewpoints