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一种基于特征统计的动态伪装效果评估方法
引用本文:杨鑫,许卫东,贾其. 一种基于特征统计的动态伪装效果评估方法[J]. 兵工学报, 2019, 40(8): 1693-1699. DOI: 10.3969/j.issn.1000-1093.2019.08.019
作者姓名:杨鑫  许卫东  贾其
作者单位:陆军工程大学野战工程学院,江苏南京,210007;陆军工程大学野战工程学院,江苏南京,210007;陆军工程大学野战工程学院,江苏南京,210007
基金项目:江苏省自然科学基金项目(BK20180579)
摘    要:现有的伪装效果评估主要针对静止的单幅图像,不能很好地模拟侦察人员对目标的判读过程。结合Mean shift目标跟踪技术,提出一种基于特征统计的动态伪装效果评估方法。该方法通过统计目标与背景8联通域的相关性特征数据,建立归一化联合高斯分布,利用概率密度的分布范围评估目标伪装效果。计算联合分布的概率密度时,提出对数放大概率,解决了高维联合分布概率密度数值敏感度低、不便于阈值设定的问题。引入样本更新策略,使样本库按照一定的概率随机更新,从而较好地适应了由于季节交替等因素引起的背景大范围变化。实验过程分别对某一指挥车实施1级伪装、2级伪装和3级伪装。采集数据后计算其对数放大概率并对曲线作出统计,结果表明:实际中划分的3种伪装状态与依据3σ准则预先设定的3种伪装状态完全对应;该模型能够有效反映出目标的伪装效果。

关 键 词:伪装效果  效果评估  特征统计  概率密度  高斯分布  目标跟踪
收稿时间:2018-09-14

A Dynamic Camouflage Effect Evaluation Method Based on Feature Statistics
YANG Xin,XU Weidong,JIA Qi. A Dynamic Camouflage Effect Evaluation Method Based on Feature Statistics[J]. Acta Armamentarii, 2019, 40(8): 1693-1699. DOI: 10.3969/j.issn.1000-1093.2019.08.019
Authors:YANG Xin  XU Weidong  JIA Qi
Affiliation:(Field Engineering College, Army Engineering University of PLA, Nanjing 210007, Jiangsu, China)
Abstract:The existing camouflage effect evaluation is mainly for a single still image, which can not simulate the process of the reconnaissance personnel's interpretation of a target. A feature statistics-based dynamic camouflage effect evaluation method is proposed based on mean shift target tracking algorithm. The proposed method is to establish a normalized joint Gaussian distribution by using the data of correlation features between the target and the background of eight-way domain, and use the distribution range of probability density to evaluate the camouflage effect of target. A logarithmic amplification probability is proposed for calculating the probability density of joint distribution, which solves the problem that the high-dimensional joint distribution probability density has low numerical sensitivity and is inconvenient to set a threshold. At the same time, a sample update strategy is introduced to make the sample library update randomly according to a certain probability, so as to better adapt to the change in large-scale background caused by the turn of seasons and other factors. In the experimental process, the first-level, second-level and third-level camouflages are applied to a certain command vehicle. After collecting the data, the logarithmic amplification probability is calculated, and the statistics on the curves is made. The results show that 3 camouflages divided in reality completely correspond to the first-level,second-level and third-level camouflages pre-set by the 3σ criterion. The experimental results show that the model can effectively reflect the camouflage effect of target.
Keywords:camouflage effect   effect evaluation   feature statistics   probability density   Gaussian distribution   target tracking  
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