Minematsu Tsubasa, Shimada Atsushi, Uchiyama Hideaki, Charvillat Vincent, Taniguchi Rin-Ichiro
Graduate School of Information Science and Electrical Engineering, Kyushu University, 744, Motooka, Nishi-ku, Fukuoka 819-0395, Japan.
IRIT, Université de Toulouse, CNRS, 31000 Toulouse, France.
Sensors (Basel). 2018 Apr 17;18(4):1232. doi: 10.3390/s18041232.
Reconstruction-based change detection methods are robust for camera motion. The methods learn reconstruction of input images based on background images. Foreground regions are detected based on the magnitude of the difference between an input image and a reconstructed input image. For learning, only background images are used. Therefore, foreground regions have larger differences than background regions. Traditional reconstruction-based methods have two problems. One is over-reconstruction of foreground regions. The other is that decision of change detection depends on magnitudes of differences only. It is difficult to distinguish magnitudes of differences in foreground regions when the foreground regions are completely reconstructed in patch images. We propose the framework of a reconstruction-based change detection method for a free-moving camera using patch images. To avoid over-reconstruction of foreground regions, our method reconstructs a masked central region in a patch image from a region surrounding the central region. Differences in foreground regions are enhanced because foreground regions in patch images are removed by the masking procedure. Change detection is learned from a patch image and a reconstructed image automatically. The decision procedure directly uses patch images rather than the differences between patch images. Our method achieves better accuracy compared to traditional reconstruction-based methods without masking patch images.
基于重建的变化检测方法对相机运动具有鲁棒性。这些方法基于背景图像学习输入图像的重建。基于输入图像与重建后的输入图像之间差异的大小来检测前景区域。在学习过程中，仅使用背景图像。因此，前景区域的差异比背景区域大。传统的基于重建的方法存在两个问题。一个是前景区域的过度重建。另一个是变化检测的决策仅依赖于差异的大小。当前景区域在补丁图像中被完全重建时，很难区分前景区域中差异的大小。我们提出了一种使用补丁图像的自由移动相机基于重建的变化检测方法框架。为了避免前景区域的过度重建，我们的方法从围绕中心区域的区域重建补丁图像中的一个掩码中心区域。由于补丁图像中的前景区域通过掩码过程被去除，前景区域的差异得到增强。变化检测是从补丁图像和重建图像中自动学习的。决策过程直接使用补丁图像而不是补丁图像之间的差异。与不掩码补丁图像的传统基于重建的方法相比，我们的方法实现了更高的准确性。
