Motion trajectory tracking algorithm based on global motion compensation in dynamic background

WANG Shan; WU Qin

doi:10.13873/J.1000-9787(2016)08-0137-04

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Motion trajectory tracking algorithm based on global motion compensation in dynamic background

更新时间：2025-09-11

- Motion trajectory tracking algorithm based on global motion compensation in dynamic background
- Vol. 35, Issue 8, Pages: 137-140(2016)
- 作者机构：
  
  江南大学物联网工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)08-0137-04
  CLC： TP391.41
- Published：2016
- 稿件说明：
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[1]王闪,吴秦.全局运动补偿的动态背景下运动轨迹跟踪算法[J].传感器与微系统,2016,35(08):137-140.

WANG Shan, WU Qin. Motion trajectory tracking algorithm based on global motion compensation in dynamic background[J]. 2016, 35(8): 137-140.
[1]王闪,吴秦.全局运动补偿的动态背景下运动轨迹跟踪算法[J].传感器与微系统,2016,35(08):137-140. DOI： 10.13873/J.1000-9787(2016)08-0137-04.

WANG Shan, WU Qin. Motion trajectory tracking algorithm based on global motion compensation in dynamic background[J]. 2016, 35(8): 137-140. DOI： 10.13873/J.1000-9787(2016)08-0137-04.

摘要

针对动态背景下

一般跟踪算法存在着被动跟踪的滞后或偏移的问题

提出了一种结合Kalman滤波器的Mean-Shift跟踪算法。对运动矢量进行预处理

得到一个平稳更能反映运动信息的矢量场;利用Mean-Shift搜索算法精确地确定对象位置;此基础上

利用Kalman滤波器算法进行运动估计预测

来确定运动的轨迹。实验表明:与现有的方法相比

该方法可从复杂场景中更准确地对运动对象进行轨迹的跟踪。

Abstract

Dynamic background

general tracking algorithm has problem of passive tracking lag or shift

propose an algorithm which combines Kalman filter and Mean-Shift tracking algorithm. Preprocess motion vector to get a smooth vector field which can reflect movement information very well; using Mean-Shift searching algorithm to determine object position precisely; motion estimation prediction is carried out by using Kalman filter algorithm on this basis

to determine movement track. Experiment shows that this method can track trajectory more accurately from complex scene compared with existing methods.

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references

随机布署平面传感器阵列实现色噪声下运动节点跟踪算法 [J]. 聂卫科,朱从光,房鼎益,陈晓江,冯大政. 传感器与微系统 . 2014(12)

基于卡尔曼滤波的移动机器人运动目标跟踪 [J]. 李庆瀛,褚金奎,李荣华,王洪青. 传感器与微系统 . 2008(11)

Object tracking in surveillance videos using compressed domain features from scalable bit-streams [J] . K. Mehmood,M. Mrak,J. Calic,A. Kondoz. Signal Processing: Image Communication . 2009 (10)

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Incremental Learning for Robust Visual Tracking [J] . David A. Ross,Jongwoo Lim,Ruei-Sung Lin,Ming-Hsuan Yang. International Journal of Computer Vision . 2008 (1)

EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation. [J] . Michael J. Black,Allan D. Jepson. International Journal of Computer Vision . 1998 (1)

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