Self-supervised Simultaneous Alignment and Change Detection
Self-supervised Simultaneous Alignment and Change Detection
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DOI：
10.1109/iros45743.2020.9340840
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
Yukuko Furukawa;K. Suzuki;Ryuhei Hamaguchi;M. Onishi;Ken Sakurada
中科院分区：
文献类型：
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作者：
Yukuko Furukawa;K. Suzuki;Ryuhei Hamaguchi;M. Onishi;Ken Sakurada
本研究提出了一种自监督的方法，用于检测场景变化的图像对。对于行车记录仪等移动的摄像机，为了减少摄像机视点的差异，必须同时优化图像对齐和变化检测，因为它们相互依赖。此外，光照条件使得场景变化检测更加困难，因为在不同时间拍摄的图像中，场景变化很大。为了解决这些挑战，我们提出了一个自我监督的同步对齐和变化检测网络（SACD-Net）。所提出的网络是鲁棒的，特别是在相机视点和光照条件的差异，同时估计翘曲参数和多尺度变化概率图，而变化区域不考虑计算的特征一致性和语义损失。基于我们的自监督模型和以前的监督模型之间的比较分析以及SACD网络的损失的消融研究，结果表明，所提出的方法使用合成数据集和我们的新的真实的数据集的有效性。
This study proposes a self-supervised method for detecting scene changes from an image pair. For mobile cameras such as drive recorders, to alleviate the camera viewpoints’ difference, image alignment and change detection must be optimized simultaneously because they depend on each other. Moreover, lighting condition makes the scene change detection more difficult because it widely varies in images taken at different times. To solve these challenges, we propose a self-supervised simultaneous alignment and change detection net-work (SACD-Net). The proposed network is robust specifically in differences of camera viewpoints and lighting conditions to simultaneously estimate warping parameters and multi-scale change probability maps while change regions are not taken into account of calculation of the feature consistency and semantic losses. Based on comparative analysis between our self-supervised and the previous supervised models as well as ablation study of the losses of SACD-Net, the results show the effectiveness of the proposed method using a synthetic dataset and our new real dataset.