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Invariant Color Model-Based Shadow Removal in Traffic Image and a New Metric for Evaluating the Performance of Shadow Removal Methods

机译:基于不变的颜色模型的影像在流量图像中删除和新的度量标准,用于评估阴影清除方法的性能

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To track objects in a traffic image sequence, objects must be extracted first. Background differencing is frequently used to extract objects. When objects are extracted, it is quite possible that shadows are included. With shadow it is not easy to do precise tracking. Thus shadows need to be removed. To do this, we proposed invariant color-based shadow removal method. Many shadow removal methods were proposed. To compare the quality of methods, several metrics were suggested. However, they suffer from inconsistency where qualitative and quantitative results do not coincide. In this paper, we proposed a new metric having such consistency.
机译:要跟踪流量图像序列中的对象,必须首先提取对象。背景技术差异经常用于提取对象。当提取对象时,很可能包括阴影。带阴影,精确跟踪并不容易。因此需要删除阴影。为此,我们提出了不变基于颜色的阴影清除方法。提出了许多阴影去除方法。为了比较方法质量,建议了几个指标。然而,它们遭受不一致的,定性和定量结果不一致。在本文中,我们提出了一种具有这种稠度的新度量。

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