光电工程, 2019, 46 (4): 180120, 网络出版: 2019-05-04  

改进萤火虫优化算法在运动阴影去除方面的应用

Application of improved firefly optimization algorithm in motion shadow removal
作者单位
上海理工大学光电信息与计算机工程学院, 上海 200093
摘要
运动阴影与目标物体粘连, 具有运动一致性, 常常被误检测为运动目标的一部分。运动阴影的存在改变了运动物体的形状, 影响运动目标前景的进一步分析。为了解决这一问题, 提出了一种基于改进萤火虫优化算法的运动阴影去除算法。通过基于种群历史最佳位置影响的改进萤火虫算法(IFA)优化 2-Otsu(二维最大类间差法)距离测度函数的寻优过程, 获得最佳阈值, 并以此进行图像分割, 去除运动阴影, 并同传统 2-Otsu法、粒子群算法(PSO)优化 2-Otsu法、萤火虫算法(FA)优化 2-Otsu法进行比较。实验结果证明, 该方法较其他三种方法分别快 2.69倍, 1.42倍, 1.21倍; 另外, 在区域一致性、阴影检测率和识别率方面均优于其他三种算法, 验证了方法的有效性。
Abstract
The motion shadow is conglutinouswith the object, and has the consistency of motion. It is often misde-tected as a part of the moving target. The existence of motion shadowchanges the shape of the moving object and influences the further analysis of the foreground of the moving target. To solve this problem, a motion shadow re-moval algorithm based on improved firefly optimization algorithm is proposed. The optimal threshold is obtained by optimizing the 2-Otsu distance measure function based on the improved glowworm algorithm which is based on the influence of the best position in the population history, and then the image is segmented and the moving shadow is removed. Compared our method with the traditional 2-Otsu method, particle swarm optimization (PSO) optimize 2-Otsu method, firefly optimization algorithm (FA) optimize 2-Otsu method, the experimental results show that the algorithm are 2.69, 1.42 and 1.21 times faster than the other three methods in the presence of shadow. Besides, it is superior to the other three algorithms in terms of region consistency, shadow detection rate and recognition rate. The effectiveness of the method is verified.

刘磊, 曹民, 张晓. 改进萤火虫优化算法在运动阴影去除方面的应用[J]. 光电工程, 2019, 46(4): 180120. Liu Lei, Cao Min, Zhang Xiao. Application of improved firefly optimization algorithm in motion shadow removal[J]. Opto-Electronic Engineering, 2019, 46(4): 180120.

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