Real-time correlation filtering target tracking based on spatio-temporal feature

ZHAO Ao-dong; SHI Lei; LIU Fei

doi:10.13873/J.1000-9787(2016)08-0119-04

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Real-time correlation filtering target tracking based on spatio-temporal feature

更新时间：2025-09-11

- Real-time correlation filtering target tracking based on spatio-temporal feature
- Vol. 35, Issue 8, Pages: 119-122+126(2016)
- 作者机构：
  
  1. 江南大学物联网工程学院
  2. 无锡职业技术学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2016)08-0119-04
  CLC： TP391.41
- Published：2016
- 稿件说明：
移动端阅览
[1]赵翱东,石磊,刘飞.基于时空特征的实时相关滤波目标跟踪[J].传感器与微系统,2016,35(08):119-122+126.

ZHAO Ao-dong, SHI Lei, LIU Fei. Real-time correlation filtering target tracking based on spatio-temporal feature[J]. 2016, 35(8): 119-122+126.
[1]赵翱东,石磊,刘飞.基于时空特征的实时相关滤波目标跟踪[J].传感器与微系统,2016,35(08):119-122+126. DOI： 10.13873/J.1000-9787(2016)08-0119-04.

ZHAO Ao-dong, SHI Lei, LIU Fei. Real-time correlation filtering target tracking based on spatio-temporal feature[J]. 2016, 35(8): 119-122+126. DOI： 10.13873/J.1000-9787(2016)08-0119-04.

摘要

提出一种基于时空信息的相关滤波实时目标跟踪算法

以解决纹理改变、光照变化、旋转变形甚至遮挡情况下目标跟踪效率问题。此算法考虑到目标定位过程中

目标与其周围信息存在空域上的相关性

来构建目标空域滤波模型

并且该算法引入时间变量来在线更新得到时空域滤波模型

利用相邻帧信息相似性来降低遮挡对跟踪的影响。通过实验对比

验证了当目标受到非理想因素干扰时

所提算法具有健壮的鲁棒性和良好实时性。

Abstract

To solve efficient problem of object tracking in terms of texture changes

illumination changes

rotational changes and even occlusion

propose a real time target tracking algorithm based on correlation filtering of spatial-temporal information. The proposed algorithm has considered that the object is relative to surrounding information in spatial area

which can be used to construct target spatial filtering model in the process of target localization. Moreover

this algorithm introduces temporal variables to update spatial filtering model online

using information similarity between consecutive frames to decrease effects of occlusion on tracking. Through experiment contrast

it is verified that the proposed algorithm has strong robustness and good real-time performance when the target is under interference of non-ideal factors.

关键词

Keywords

references

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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