An Object Tracking Approach Based on Quantum Genetic Algorithm

JIN Ze-fen-fen; HOU Zhi-qiang; YU Wang-sheng; WANG Xin; KOU Ren-ke

doi:10.3969/j.issn.0372-2112.2020.08.006

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An Object Tracking Approach Based on Quantum Genetic Algorithm

更新时间：2025-07-16

- An Object Tracking Approach Based on Quantum Genetic Algorithm
- Acta Electronica Sinica Vol. 48, Issue 8, Pages: 1493-1501(2020)
- 作者机构：
  
  1. 空军工程大学信息与导航学院,陕西,西安,710077
  2. 中国人民解放军95959部队,北京,100195
  3. 中国人民解放军93665部队,山西,忻州,036200
  4. 中国人民解放军95084部队,广东,佛山,528226
  5. 空军工程大学信息与导航学院,陕西,西安,710077
  6. 中国人民解放军95959部队,北京,100195
  7. 中国人民解放军93665部队,山西,忻州,036200
  8. 中国人民解放军95084部队,广东,佛山,528226
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China (No.61703423)
- DOI：10.3969/j.issn.0372-2112.2020.08.006
  CLC： TP391.4
- Published Online：25 August 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
JIN Ze-fen-fen, HOU Zhi-qiang, YU Wang-sheng, et al. An Object Tracking Approach Based on Quantum Genetic Algorithm[J]. Acta Electronica Sinica, 2020, 48(8): 1493-1501.
DOI：

JIN Ze-fen-fen, HOU Zhi-qiang, YU Wang-sheng, et al. An Object Tracking Approach Based on Quantum Genetic Algorithm[J]. Acta Electronica Sinica, 2020, 48(8): 1493-1501. DOI： 10.3969/j.issn.0372-2112.2020.08.006.

摘要

针对视觉目标跟踪中传统搜索方法效率不高、难以求取全局最优等问题，利用量子遗传算法的全局寻优能力，提出了一种采用量子遗传算法作为搜索策略的视觉跟踪方法.在量子遗传算法的框架下，将像素点位置作为种群中的个体，提取颜色直方图作为特征，以相似性度量作为目标函数计算个体适应度值，找出相似度最大的像素点位置输出，最终完成跟踪.实验结果表明，本文方法在目标速度快、遮挡和非刚性形变等情况下具有明显优势，且算法运算量小，跟踪速度快.

Abstract

Aiming at the problem that traditional search method in visual tracking is not efficient and the global optimization is hard to be solved

as the global optimization ability of quantum genetic algorithm

we put forward a visual tracking method by using quantum genetic algorithm as the search strategy. In the framework of quantum genetic algorithm

regard the pixel positions as the individuals in the population

and extract the color histogram as characteristics. The individual fitness are calculated by taking similarity measure as the objective function. We find out the maximum similarity and output its homologous position

to finish the tracking. The experimental results show that the algorithm has obvious advantages in fast speed

occlusion and non-rigid deformation

and the tracking speed is fast.

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