基于量子遗传算法的视觉目标跟踪

doi:10.3969/j.issn.0372-2112.2020.08.006

电子学报 ›› 2020, Vol. 48 ›› Issue (8): 1493-1501.DOI: 10.3969/j.issn.0372-2112.2020.08.006

基于量子遗传算法的视觉目标跟踪

金泽芬芬^1,2, 侯志强¹, 余旺盛¹, 王鑫^1,3, 寇人可⁴

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

收稿日期:2016-12-23 修回日期:2020-01-08 出版日期:2020-08-25
- 通讯作者:
- 金泽芬芬
- 作者简介:
- 侯志强 男,1973年出生于陕西眉县,2005年获西安交通大学工学博士学位,现西安邮电大学计算机学院教授,主要研究方向为图像处理、计算机视觉、无人机应用以及信息融合等. E-mail:hou-zhq@sohu.com
- 基金资助:
- 国家自然科学基金 (No.61703423）

An Object Tracking Approach Based on Quantum Genetic Algorithm

JIN Ze-fen-fen^1,2, HOU Zhi-qiang¹, YU Wang-sheng¹, WANG Xin^1,3, KOU Ren-ke⁴

1. Institute of Information and Navigation, Air Force Engineering University, Xi'an, Shaanxi 710077, China;
2. Unit 95959 of Chinese People's Liberation Army, Beijing 100195, China;
3. Unit 93665 of Chinese People's Liberation Army, Xinzhou, Shanxi 036200, China;
4. Unit 95084 of Chinese People's Liberation Army, Foshan, Guangdong 528226, China

Received:2016-12-23 Revised:2020-01-08 Online:2020-08-25 Published:2020-08-25
- Corresponding author:
- JIN Ze-fen-fen
- Supported by:
- National Natural Science Foundation of China (No.61703423)

摘要/Abstract

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.

关键词: 视觉跟踪, 量子遗传算法, 颜色特征

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.

Key words: visual tracking, quantum genetic algorithm, color feature

中图分类号:

TP391.4

金泽芬芬, 侯志强, 余旺盛, 等. 基于量子遗传算法的视觉目标跟踪[J]. 电子学报, 2020, 48(8): 1493-1501.

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.

参考文献

[1] 薛模根,朱虹,袁广林.在线鲁棒判别式字典学习视觉跟踪[J].电子学报,2016,44(4):838-845. XUE Mo-gen,ZHU Hong,YUAN Guang-lin.Online robust discrimination dictionary learning for visual tracking[J].Acta Electronica Sinica,2016,44(4):838-845.(in Chinese)
[2] KRISTAN M,MATAS J,LEONARDIS A,et al.The visual object tracking VOT2015 challenge results[J].IEEE International Conference on Computer Vision Workshops,2016,6(3):564-586.
[3] 范舜奕,管桦,侯志强,等.基于多表观特征子模型更新的鲁棒视觉跟踪[J].电子学报,2018,46(2):440-446. FAN Shun-yi,GUAN Hua,HOU Zhi-qiang,et al.Robust visual tracking based on sub-model updating of multiple apparent features[J].Acta Electronica Sinica,2018,46(2):440-446.(in Chinese)
[4] WU Y,LIM J,YANG M H.Online object tracking:a benchmark[J].IEEE Conference on Computer Vision & Pattern Recognition,2013,9(4):2411-2418.
[5] 宋涛,李鸥,刘广怡,等.基于改进协作目标外观模型的在线视觉跟踪[J].电子学报,2017,45(2):384-393. SONG Tao,LI Ou,LIU Guang-yi,et al.Online visual tracking based on improved collaborative appearance model[J]. Acta Electronica Sinica,2017,45(2):384-393.(in Chinese)
[6] ISARD M,BLAKE A.CONDENSATION-conditional density propagation for visual tracking[J].International Journal of Computer Vision,1998,29(1):5-28.
[7] COMANICIU D,RAMESH V,MEER P.Kernel-based object tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2003,25(5):564-575.
[8] KALAL Z,MATAS J,MIKOLAJCZYK K.P-N Learning:Bootstrapping binary classifiers by structural constraints[J].IEEE Conference on Computer Vision and Pattern Recognition,2010,238(6):49-56.
[9] 焦建彬,叶齐祥,韩振军,等.视觉目标检测与跟踪[M].北京:科学出版社,2015. JIAO Jianbin,YE Qixiang,HAN Zhenjun,et al.Visual Target Detection and Tracking[M].Beijing:Science Press,2015.(in Chinese)
[10] BABENKO B,YANG M H,BELONGIE S.Visual tracking with online multiple instance learning[J].IEEE Conference on Computer Vision and Pattern Recognition,2009,33(8):983-990.
[11] HAN K H,KIM J H.Quantum-inspired evolutionary algorithms with a new termination criterion,he gate,and two-phase scheme[J].IEEE Transactions on Evolutionary Computation,2004,8(2):156-169.
[12] 张磊,方洋旺,柴栋,等.基于改进量子进化算法的巡航导弹航路规划方法[J].兵工学报,2015,35(11):1820-1827. ZHANG Lei,FANG Yangwang,CHAI Dong,et al.Cruise missile path planning based on improved quantum evolutionary algorithm[J].Acta Armamentarii,2015,35(11):1820-1827.(in Chinese)
[13] 樊富有,杨国武,乐千桤,等.基于量子遗传算法的无线视频传感网络优化覆盖算法[J].通信学报,2015,(6):94-104. FAN Fuyou,YANG Guowu,LE Qianqi,et al.Optimized coverage algorithm of wireless video sensor network based on quantum genetic algorithm[J].Journal on Communications,2015,(6):94-104.(in Chinese)
[14] 王涛,王洋洋,郭长娜,等.QGA-RBF神经网络在矿井瓦斯涌出量预测中的应用[J].传感技术学报,2012,25(1):119-123. WANG Tao,WANG Yangyang,GUO Changna,et al.Application of QGA-RBF for predicting the amount of mine gas emission[J].Chinese Journal of Sensors and Actuators,2012,25(1):119-123.(in Chinese)
[15] HAN K H,KIM J H.Genetic quantum algorithm and its application to combinatorial optimization problem[J].Congress on Evolutionary Computation,2003,2(2):1354-1360.
[16] ZHANG T,GHANEM B,Liu S,et al.Robust visual tracking via multi-task sparse learning[J].IEEE Conference on Computer Vision and Pattern Recognition,2012,157(10):2042-2049.
[17] KRISTAN M,PFLUGFELDER R,LEONARDIS A,et al.The visual object tracking VOT2013 challenge results[A].International Conference on Computer Vision Workshops[C].Sydney,Australia:IEEE,2015,8926:98-111.
[18] JIA X,LU H,YANG M H.Visual tracking via adaptive structural local sparse appearance model[J].IEEE Conference on Computer Vision and Pattern Recognition,2012,157(10):1822-1829.
[19] LAURA S L,ERIK L M.Distribution fields for tracking[J].IEEE Conference on Computer Vision and Pattern Recognition,2012,157(10):1910-1917.
[20] ZHANG K,ZHANG L,YANG M H.Real-time compressive tracking[A].European Conference on Computer Vision[C].Florence,Italy,2012.864-877.
[21] COLLINS R T,LIU Y,Leordeanu M.Online selection of discriminative tracking features[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1631-1643.
[22] WU Y,SHEN B,LING H.Online robust image alignment via iterative convex optimization[J].IEEE Conference on Computer Vision and Pattern Recognition,2012,157(10): 1808-1814.
[23] KALAL Z,MATAS J,MIKOLAJCZYK K.P-N learning:Bootstrapping binary classifiers by structural constraints[J].IEEE Conference on Computer Vision & Pattern Recognition,2010,238(6):49-56.

基于量子遗传算法的视觉目标跟踪

An Object Tracking Approach Based on Quantum Genetic Algorithm

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