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
An Object Tracking Approach Based on Quantum Genetic Algorithm
JIN Ze-fen-fen1,2 , HOU Zhi-qiang1 , YU Wang-sheng1 , WANG Xin1,3 , 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
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
收稿日期: 2016-12-23
出版日期: 2020-04-13
基金资助: 国家自然科学基金(No.61703423)
通讯作者:
金泽芬芬
E-mail: christine123456@163.com
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
引用本文:
金泽芬芬, 侯志强, 余旺盛, 王鑫, 寇人可. 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, WANG Xin, KOU Ren-ke. An Object Tracking Approach Based on Quantum Genetic Algorithm. Acta Electronica Sinica, 2020, 48(8): 1493-1501.
链接本文:
http://www.ejournal.org.cn/CN/10.3969/j.issn.0372-2112.2020.08.006 或 http://www.ejournal.org.cn/CN/Y2020/V48/I8/1493
[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.
[1]
仇祝令, 查宇飞, 吴敏, 王青. 基于注意力学习的正则化相关滤波跟踪算法 [J]. 电子学报, 2020, 48(9): 1762-1768.
[2]
蒲磊, 冯新喜, 侯志强, 余旺盛. 基于二阶池化网络的鲁棒视觉跟踪算法 [J]. 电子学报, 2020, 48(8): 1472-1478.
[3]
张峰, 钟宝江. 基于兴趣目标的图像检索 [J]. 电子学报, 2018, 46(8): 1915-1923.
[4]
范舜奕, 管桦, 侯志强, 余旺盛, 戴铂. 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.
[5]
孙巧, 张胜修, 张正新, 曹立佳, 李小锋. 基于加权重叠率的单目标视觉跟踪评价指标 [J]. 电子学报, 2017, 45(3): 753-761.
[6]
宋涛, 李鸥, 刘广怡, 崔弘亮. 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.
[7]
余旺盛, 李卫华, 侯志强. 分层结构直方图及其应用 [J]. 电子学报, 2017, 45(11): 2617-2624.
[8]
宋涛, 李鸥, 刘广怡. 基于置信区域内多级动态层表达的类贯序蒙特卡洛视觉跟踪方法 [J]. 电子学报, 2016, 44(6): 1355-1361.
[9]
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.
[10]
袁广林, 薛模根. 基于主分量寻踪的鲁棒视觉跟踪 [J]. 电子学报, 2015, 43(3): 417-423.
[11]
余旺盛, 田孝华, 侯志强, 查宇飞. 基于局部分块学习的在线视觉跟踪 [J]. 电子学报, 2015, 43(1): 74-78.
[12]
周燕, 曾凡智, 赵慧民, 卢炎生, 周月霞. 一种基于精细化稀疏自适应匹配追踪算法的图像检索方法研究 [J]. 电子学报, 2014, 42(12): 2457-2466.
[13]
余旺盛, 田孝华, 侯志强, 黄安奇, 刘翔. 基于关键区域特征匹配的视觉跟踪算法 [J]. 电子学报, 2014, 42(11): 2150-2156.
[14]
余旺盛, 侯志强, 田孝华. 基于旋转不变直方图的快速匹配穷搜索 [J]. 电子学报, 2012, 40(11): 2177-2182.
[15]
汪鹏君;李辉;吴文晋;王伶俐;张小颖;戴静. 量子遗传算法在多输出Reed-Muller逻辑电路最佳极性搜索中的应用 [J]. 电子学报, 2010, 38(5): 1058-1063.