Motion analysis is important in video surveillance systems and background subtraction is useful for moving object detection in such systems. However, most of the existing background subtraction methods do not work well for surveillance systems in the evening because objects are usually dark and reflected light is usually strong. To resolve these issues, we propose a framework that utilizes a Weber contrast descriptor, a texture feature extractor, and a light detection unit, to extract the features of foreground objects. We propose a local pattern enhancement method. For the light detection unit, our method utilizes the finding that lighted areas in the evening usually have a low saturation in hue-saturation-value and hue-saturation-lightness color spaces. Finally, we update the background model and the foreground objects in the framework. This approach is able to improve foreground object detection in night videos, which do not need a large data set for pre-training.

中文翻译：

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夜间视频的背景减法

运动分析在视频监控系统中很重要，而背景减法对于此类系统中的运动物体检测很有用。然而，大多数现有的背景减法方法不适用于晚上的监视系统，因为物体通常很暗，反射光通常很强。为了解决这些问题，我们提出了一个框架，该框架利用韦伯对比度描述符、纹理特征提取器和光检测单元来提取前景对象的特征。我们提出了一种局部模式增强方法。对于光检测单元，我们的方法利用了晚上的照明区域通常在色调饱和度值和色调饱和度亮度颜色空间中具有低饱和度的发现。最后，我们更新框架中的背景模型和前景对象。