Research of Moving Object Detection Algorithm Based on Non-restraint Learning and Shadow Removal Algorithm

Jiyuan Zhang, Xue Bai
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Abstract

A detection algorithm of moving objects which is robust and effective is proposed for the problem of the chaos and variety scenes. The background model based color quickly is build during the nonrestraint learning. According to the background of the chaos, it updates the model. However, shadow is also detected as moving object because of the same characters between them, so the algorithm of shadow removal based on its characteristic of chroma, lightness and crossover entropy was presented too. Finally, the algorithm was simulated in real-time and experimental effect, and achieved better results.
基于无约束学习和阴影去除算法的运动目标检测算法研究
针对混沌多变的场景,提出了一种鲁棒有效的运动目标检测算法。在无约束学习过程中快速建立基于颜色的背景模型。根据混沌的背景,对模型进行更新。然而,由于阴影之间具有相同的特征,因此也会被检测为运动物体,因此提出了基于阴影的色度、亮度和交叉熵特征的阴影去除算法。最后,对该算法进行了实时仿真和实验效果验证,取得了较好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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