计算机应用 ›› 2011, Vol. 31 ›› Issue (09): 2493-2496.DOI: 10.3724/SP.J.1087.2011.02493

• 图形图像技术 • 上一篇    下一篇

复杂背景下感兴趣运动目标的跟踪算法

冯晓敏,郭继昌,张艳   

  1. 天津大学 电子信息工程学院,天津 300072
  • 收稿日期:2011-03-22 修回日期:2011-05-31 发布日期:2011-09-01 出版日期:2011-09-01
  • 通讯作者: 冯晓敏
  • 作者简介:冯晓敏(1986-),女,河南濮阳人,硕士研究生,主要研究方向:图像处理、计算机视觉;
    郭继昌(1966-),男,河北沧州人,教授,博士,主要研究方向:数字图像处理、DSP应用系统、模拟滤波器;
    张艳(1986-),女,河北保定人,硕士研究生,主要研究方向:图像处理、计算机视觉。
  • 基金资助:
    天津市科技支撑计划项目(10ZCKFGX00700)

Tracking algorithm of interested moving target under complex background

FENG Xiao-min1,GUO Ji-chang2,ZHANG Yan2   

  1. 1.
    2. School of Electronic Information Engineering,Tianjin University,Tianjin 300072,China
  • Received:2011-03-22 Revised:2011-05-31 Online:2011-09-01 Published:2011-09-01
  • Contact: FENG Xiao-min

摘要: 针对由于复杂背景的干扰而导致不能准确跟踪感兴趣运动目标的问题,提出一种基于多特征自适应融合的粒子滤波跟踪算法。首先在HSV颜色空间中得到感兴趣运动目标的加权颜色分布模型,然后利用不变矩特征来消除背景中相似颜色物体和光照变化的干扰,两种特征通过自适应调整权重来更新粒子权值而融合于粒子滤波算法中,从而能够准确和稳定地跟踪运动目标。实验证明,该算法在运动目标平移、姿态变化、遮挡、光照变化及相似颜色干扰等复杂背景下都能够准确地进行跟踪,对背景干扰具有很强的鲁棒性。

关键词: 加权颜色分布, 不变矩, 自适应融合, 粒子权值, 粒子滤波

Abstract: Concerning the problem of tracking interested moving target inaccurately because of complex background, a robust tracking algorithm based on adaptive multi-feature fusion was proposed. First, the algorithm obtained the weighted color distribution model of interested moving target in the HSV color space. Then invariant moment was used to eliminate the interference of the similar background color and illumination changes. The algorithm fused the two features in the particle filter by adjusting their weights and updating particle weights adaptively. Thus, the algorithm can track the moving target accurately and stably. The experimental results show that the algorithm can track interested moving target accurately when moving target is tracked under complex background such as translation, variant posture, and be blocked of the moving object, varying illumination and the interference of the similar background color. The algorithm has strong robustness to background interference.

Key words: weighted color distribution, invariant moment, adaptive fusion, particle weight, particle filter

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