Pixel feature-weighted scale-adaptive object tracking algorithm

Hui-lan LUO; Fang-fang DU; Fan-sheng KONG

doi:10.11959/j.issn.1000-436x.2015259

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Pixel feature-weighted scale-adaptive object tracking algorithm

academic paper | 更新时间：2024-06-05

- Pixel feature-weighted scale-adaptive object tracking algorithm
- Journal on Communications Vol. 36, Issue 10, Pages: 200-210(2015)
- 作者机构：
  
  1. 江西理工大学信息工程学院，江西赣州 341000
  2. 浙江大学计算机科学技术学院，浙江杭州 310027
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61105042);The National Natural Science Foundation of China(61462035);The Science and Technology Foun-dation of Education Department of Jiangxi Province(GJJ13421)
- DOI：10.11959/j.issn.1000-436x.2015259
  CLC： TP391
- Online First：2015-10，
  
  Published：25 October 2015
- 稿件说明：
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Hui-lan LUO, Fang-fang DU, Fan-sheng KONG. Pixel feature-weighted scale-adaptive object tracking algorithm[J]. Journal on Communications, 2015, 36(10): 200-210.
DOI：

Hui-lan LUO, Fang-fang DU, Fan-sheng KONG. Pixel feature-weighted scale-adaptive object tracking algorithm[J]. Journal on Communications, 2015, 36(10): 200-210. DOI： 10.11959/j.issn.1000-436x.2015259.

摘要

针对目标运动过程中的姿态变化、旋转、干扰以及缩放等情况，提出了结合像素点特征加权的尺度自适应跟踪算法。首先利用目标区域中每个像素点的颜色特征和位置特征，建立目标模型；其次用目标的平均权值图估算尺度变化系数，以实现目标尺度的自适应；最后构建一个更新模型，对跟踪过程中的目标模型和背景模型进行更新。实验表明，提出的算法充分利用目标区域内各像素点间的差异，可以做到快速、有效的跟踪，且具有较强的顽健性。

Abstract

An effective object tracking method using weighted pixel features was proposed to deal with all kinds of complicated tracking situations

such as target movement

rotation

background interference and scaling and so on.First

the color feature and location information of the pixels in the target area were used to build the object model.Then the average weight image was used to estimate the scale variation coefficient.The aim was to adapt to the scale changes of the target.Finally

an update model was proposed

which was able to renew the object model and background model.The experimental results show that the proposed algorithm could make full use of the differences between pixels in the target area

so it can track more quickly and more effectively with strong robustness.

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Keywords

references

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