孙一南, 韩晓明. 基于多算法融合的运动目标定位研究[J]. 微电子学与计算机, 2017, 34(8): 123-127, 132.
引用本文: 孙一南, 韩晓明. 基于多算法融合的运动目标定位研究[J]. 微电子学与计算机, 2017, 34(8): 123-127, 132.
SUN Yi-nan, HAN Xiao-ming. Research of Moving Object Localization Based on Multi Fusion Algorithm[J]. Microelectronics & Computer, 2017, 34(8): 123-127, 132.
Citation: SUN Yi-nan, HAN Xiao-ming. Research of Moving Object Localization Based on Multi Fusion Algorithm[J]. Microelectronics & Computer, 2017, 34(8): 123-127, 132.

基于多算法融合的运动目标定位研究

Research of Moving Object Localization Based on Multi Fusion Algorithm

  • 摘要: 在固定摄像头的情况下,背景消减法是用来提取视频中运动目标的最有效方法之一,而背景消减法的关键在于背景建模算法和背景与前景的消减方式,针对背景消减法在复杂背景下目标提取时出现的目标缺损、空洞现象和相关背景建模方法计算复杂、背景模型提取时间长等不足,提出了一种多算法融合的背景消减法.该算法通过充分融合发挥RGB彩色模型的颜色特征、码本法的抗扰动性和三帧帧差法与Surendra法的快速性,提出了一种新的背景建模算法以及消减方式,使得在尽可能降低计算量的同时成功提高了算法对闪烁背景的耐受性,并解决了复杂背景条件下运动目标出现缺损与空洞的问题.

     

    Abstract: In the case of a fixed camera, background subtraction method is one of the most effective method to extract moving objects in video, and the key of the background subtraction algorithm is the background modeling method and the subtraction method between background and foreground, when in complex background, the background subtraction method may leads to target defect, empty hole and related background modeling methods are complex, which causes long extraction time, so here proposes a multi fusion background subtraction algorithm.By exerting the color characteristics of RGB color model, anti disturbance of codebook method, and rapidity of three frame difference method and Surendra method fully, a new background modeling algorithm and its reduction method are proposed, which reduces the calculation quantity, improves the tolerance of twinkling background, and solves the problem of defect and cavity of moving object in complex background successfully.

     

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