Computer Vision Center and the Department of Computer Science, Universitat Autònoma de Barcelona, 08193 Barcelona, Spain.
IEEE Trans Image Process. 2011 Oct;20(10):2954-66. doi: 10.1109/TIP.2011.2132728. Epub 2011 Mar 24.
This paper describes a novel framework for detection and suppression of properly shadowed regions for most possible scenarios occurring in real video sequences. Our approach requires no prior knowledge about the scene, nor is it restricted to specific scene structures. Furthermore, the technique can detect both achromatic and chromatic shadows even in the presence of camouflage that occurs when foreground regions are very similar in color to shadowed regions. The method exploits local color constancy properties due to reflectance suppression over shadowed regions. To detect shadowed regions in a scene, the values of the background image are divided by values of the current frame in the RGB color space. We show how this luminance ratio can be used to identify segments with low gradient constancy, which in turn distinguish shadows from foreground. Experimental results on a collection of publicly available datasets illustrate the superior performance of our method compared with the most sophisticated, state-of-the-art shadow detection algorithms. These results show that our approach is robust and accurate over a broad range of shadow types and challenging video conditions.
本文提出了一种新颖的框架，用于检测和抑制大多数实际视频序列中可能出现的适当阴影区域。我们的方法不需要事先了解场景，也不限于特定的场景结构。此外，即使在前景区域与阴影区域颜色非常相似导致伪装的情况下，该技术也可以检测到非彩色和彩色阴影。该方法利用了由于反射抑制而导致的局部颜色恒常性特性。为了在场景中检测阴影区域，将背景图像的值除以 RGB 颜色空间中当前帧的值。我们展示了如何使用这个亮度比来识别具有低梯度恒常性的片段，从而将阴影与前景区分开来。在一组公开可用的数据集上的实验结果表明，与最复杂、最先进的阴影检测算法相比，我们的方法具有优越的性能。这些结果表明，我们的方法在广泛的阴影类型和具有挑战性的视频条件下具有鲁棒性和准确性。
