Adaptive multi-scale color feature target tracking algorithm

Xiao-yun LI; Qiu-sheng HE; Wei-feng ZHAGN; Hui-hui LIANG; Wei CHEN

doi:10.3788/YJYXS20193403.0291

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Adaptive multi-scale color feature target tracking algorithm

Image Processing | 更新时间：2020-07-05

- Adaptive multi-scale color feature target tracking algorithm
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 3, Pages: 291-301(2019)
- 作者机构：
  
  1.太原科技大学电子信息工程学院, 山西太原 030024
  2.中国矿业大学计算机科学与技术学院, 江苏徐州 221116
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20193403.0291
  CLC： TP311
- Received：20 September 2018，
  
  Accepted：24 December 2018，
  
  Published：2019-03
- 稿件说明：
移动端阅览
Xiao-yun LI, Qiu-sheng HE, Wei-feng ZHAGN, et al. Adaptive multi-scale color feature target tracking algorithm[J]. Chinese journal of liquid crystals and displays, 2019, 34(3): 291-301.
DOI：

Xiao-yun LI, Qiu-sheng HE, Wei-feng ZHAGN, et al. Adaptive multi-scale color feature target tracking algorithm[J]. Chinese journal of liquid crystals and displays, 2019, 34(3): 291-301. DOI： 10.3788/YJYXS20193403.0291.

摘要

为了解决尺度变化对目标跟踪的影响，本文在颜色特征跟踪算法的基础下提出了一种多尺度目标跟踪算法。该算法通过训练位置和尺度两个相关滤波器以实现自适应尺度跟踪。首先通过最小二乘分类器学习获得位置相关滤波器，采用主成分分析法对颜色特征进行降维，计算响应的最大值作为下一帧目标中心位置；接着根据设定的尺度因子在中心位置周围形成多个大小不一的矩形区域，并计算每个区域的颜色特征；学习每个区域的颜色特征，获得尺度相关滤波器，并采用正交三角分解对尺度相关滤波器进行降维；然后根据响应的最大值确定跟踪目标的尺寸；最后对目标的位置和尺寸进行更新。通过对13组挑战性的视频序列进行测试，结果表明，本算法不仅对目标尺度变化具有一定的适应性，而且对光照变化、快速运动、运动模糊等复杂情况下，均具有鲁棒性，多项性能指标均优于目前跟踪性能先进的算法。

Abstract

In order to solve the influence of scale change on target tracking

a multi-scale target tracking algorithm was proposed based on the color feature tracking algorithm. The algorithm realized adaptive scale tracking by training position and scale correlation filters. Firstly

the target center position of next frame was obtained by computing the maximum of the response

where the position correlation filter was learned by the least squares classifier and the dimensionality reduction for color features was analyzed by principal component analysis. The scale correlation filter was obtained by color characteristics at 33 rectangular areas which was set by the scale factor around the central location and reduced dimensions by orthogonal triangle decomposition. Finally

the location and size of the target were updated by the maximum of the response. By testing 13 challenging video sequences

the results show that the algorithm has adaptability to the changes in the target scale and its robustness along with many other performance indicators are both better than the most state-of-the-art methods in illumination variation

fast motion

motion blur and other complex situations.

关键词

Keywords

references

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