江汉大学学报(自然科学版) ›› 2016, Vol. 44 ›› Issue (6): 547-551.doi: 10.16389/j.cnki.cn42-1737/n.2016.06.011

• 计算机科学 • 上一篇    下一篇

基于短时灰预测的像素预测模型

郑朝晖1,鞠剑平2   

  1. 1. 武汉铁路职业技术学院 公共课部,湖北 武汉 430205;2. 湖北商贸学院 机电与信息工程学院,湖北 武汉 430079
  • 出版日期:2016-12-28 发布日期:2016-12-30
  • 作者简介:郑朝晖(1985—),男,讲师,博士生,研究方向:模式识别与机器视觉。
  • 基金资助:
    武汉铁路职业技术学院院级课题(Y2015023)

Pixel Prediction Model Based on Short Time Grey Prediction

ZHENG Zhaohui1,JU Jianping2   

  1. 1. Department of Public Courses,Wuhan Railway Vocational College of Technology,Wuhan 430205,Hubei,China;2. School of Mechanical Electronic and Information Engineering,Hubei Business College,Wuhan 430079,Hubei,China
  • Online:2016-12-28 Published:2016-12-30

摘要: 提出了一种采用GM(1,1)模型预测目标特征变化的新方法。该方法通过对目标区域进行分块,计算块区域像素和,同时利用短时时间序列对像素和序列进行累加处理生成新序列,通过GM(1,1)模型得到目标的预测模型。GM(1,1)像素预测模型方法对目标具有较强的预判能力,对短时特征变化具有较好的预测能力。跟踪算法能很好地将特征变化与预测结合到一起,利用该方法进行跟踪测试,对比当前传统跟踪算法其跟踪性能有显著提高。

关键词: GM(1, 1), 像素预测, 时间序列, 目标跟踪

Abstract: A new idea with GM(1,1)model to predict the change of target feature is proposed in this paper. The method divides the target area into blocks,and calculates the sum of the pixel in the block area. At the same time,the short time series are used to cumulativly process the pixels sum sequence to generate a new sequence. The target prediction model is obtained with GM(1,1)model. GM(1,1) pixel prediction model has a strong predictive ability for the target,and has a better predictive ability for the short-term characteristics change. In this paper,a new effective tracking algorithm is proposed,which combines the feature change with the prediction. After testing the proposed method,it is found that the tracking performance is improved significantly compared with the traditional tracking algorithm.

Key words: GM(1, 1), pixel prediction, time series, target tracking

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