论文标题
现代引导的深度对象匹配场景变化检测
Epipolar-Guided Deep Object Matching for Scene Change Detection
论文作者
论文摘要
本文介绍了基于观点的基于对象的变更检测网络（OBJ-CDNET）。移动摄像机（例如驱动器记录器），由于相机轨迹和快门时间的差异，每次都会从不同的角度捕获图像。但是，以前的像素更改检测的方法很容易受到观点差异的影响，因为它们假定对齐的图像对作为输入。为了应对难度，我们引入了一个深度的图形匹配网络，该网络在图像对之间建立对象对应。引言使我们能够检测到对象场景的变化而无需精确的图像对齐。对于更准确的对象匹配，我们提出了一个表极引导的深图匹配网络（EGMNET），该网络将Epolar约束结合到OBJCDNET中使用的深图匹配层中。为了评估网络对观点差异的鲁棒性，我们创建了从图像对的场景更改检测的合成和真实数据集。实验结果验证了我们网络的有效性。
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.
