School of Computer Science and Technology, Guangxi University of Science and Technology, Liuzhou, Guangxi, China.
Business School, Guilin University of Electronic Technology, Guilin, Guangxi, China.
PLoS One. 2023 Feb 7;18(2):e0276284. doi: 10.1371/journal.pone.0276284. eCollection 2023.
Shadow removal is an important issue in the field of motion object surveillance and automatic control. Although many works are concentrated on this issue, the diverse and similar motion patterns between shadows and objects still severely affect the removal performance. Constrained by the computational efficiency in real-time monitoring, the pixel feature based methods are still the main shadow removal methods in practice. Following this idea, this paper proposes a novel and simple shadow removal method based on a differential correction calculation between the pixel values of Red, Green and Blue channels. Specifically, considering the fact that shadows are formed because of the occlusion of light by objects, all the reflected light will be attenuated. Hence there will be a similar weakening trends in all Red, Green and Blue channels of the shadow areas, but not in the object areas. These trends can be caught by differential correction calculation and distinguish the shadow areas from object areas. Based on this feature, our shadow removal method is designed. Experiment results verify that, compared with other state-of-the-art shadow removal methods, our method improves the average of object and shadow detection accuracies by at least 10% in most of the cases.
去除阴影是运动目标监测和自动控制领域的一个重要问题。尽管许多研究都集中在这个问题上，但阴影和物体之间的多样且相似的运动模式仍然严重影响着去除效果。受到实时监测中计算效率的限制，基于像素特征的方法仍然是实践中主要的阴影去除方法。基于这个思路，本文提出了一种新颖而简单的基于红、绿、蓝通道像素值差分修正计算的阴影去除方法。具体来说，考虑到阴影是由于物体遮挡光线而形成的，所有反射光都会被衰减。因此，阴影区域的所有红、绿、蓝通道都会呈现出类似的减弱趋势，但物体区域则不会。这些趋势可以通过差分修正计算来捕捉，并将阴影区域与物体区域区分开来。基于这个特点，我们设计了阴影去除方法。实验结果验证了，与其他最先进的阴影去除方法相比，我们的方法在大多数情况下至少提高了 10%的物体和阴影检测精度的平均值。
