基于全局背景与特征降维的视觉跟踪算法
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摘要
相关滤波算法容易受到形变、运动模糊、相似背景等因素的干扰,导致跟踪任务失败。为了克服以上问题,该文提出一种基于全局背景与特征降维的视觉跟踪算法。该算法首先提取紧邻目标的图像区域作为负样本供分类器学习,以抑制相似背景的干扰;然后提出一种基于主成分分析的更新策略,构建降维矩阵压缩HOG特征的维度,在更新分类器的同时减少其冗余度;最后加入颜色特征表征运动目标,并根据特征对系统状态的响应强度进行自适应融合。在标准数据集上将该文提出的算法与Staple, KCF等其他算法进行了仿真对比,结果表明该文算法具有更强的鲁棒性,在形变因素的影响下,所提出的算法与Staple和KCF算法相比距离精度分别提升8.3%和13.1%。
Tracking effects of algorithms using correlation filter are easily interfered by deformation, motion blur and background clustering, which can result in tracking failure. To solve these problems, a visual tracking algorithm based on global context and feature dimensionality reduction is proposed. Firstly, the image patches uniformly around the target are extracted as negative sample, and thus the similar background patches around the target are suppressed. Then, an update strategy based on principal component analysis is proposed,constructing the matrix to reduce the dimensionality of HOG feature, which can reduce the redundancy of feature when it updates. Finally, the color features are added to represent the motion target and the response of the system states are adaptively fused according to the features. Experiments are performed on recent online tracking benchmark. The results show that the proposed method performs favorably both in terms of accuracy and robustness compared to the state-of-the-art trackers such as Staple or KCF. When deformation occur, the proposed method is shown to outperform the Staple tracker and KCF algorithm by 8.3% and 13.1%respectively in median distance precision.
Tracking effects of algorithms using correlation filter are easily interfered by deformation, motion blur and background clustering, which can result in tracking failure. To solve these problems, a visual tracking algorithm based on global context and feature dimensionality reduction is proposed. Firstly, the image patches uniformly around the target are extracted as negative sample, and thus the similar background patches around the target are suppressed. Then, an update strategy based on principal component analysis is proposed,constructing the matrix to reduce the dimensionality of HOG feature, which can reduce the redundancy of feature when it updates. Finally, the color features are added to represent the motion target and the response of the system states are adaptively fused according to the features. Experiments are performed on recent online tracking benchmark. The results show that the proposed method performs favorably both in terms of accuracy and robustness compared to the state-of-the-art trackers such as Staple or KCF. When deformation occur, the proposed method is shown to outperform the Staple tracker and KCF algorithm by 8.3% and 13.1%respectively in median distance precision.
引文
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[18]熊昌镇,赵璐璐,郭芬红.自适应特征融合的核相关滤波跟踪算法[J].计算机辅助设计与图形学学报,2017,29(6):1068-1074.doi:10.3969/j.issn.1003-9775.2017.06.012.XIONG Changzhen,ZHAO Lulu,and GUO Fenhong.Kernelized correlation filters tracking based on adaptive feature fusion[J].Journal of Computer-Aided Design&Computer Graphics,2017,29(6):1068-1074.doi:10.3969/j.issn.1003-9775.2017.06.012.
[19]WU Yi,LIM J,and YANG Minghsuan.Online object tracking:a benchmark[C].Proceedings of the Computer Vision and Pattern Recognition,Portland,USA,2013:2411-2418.
[2]JI Hui,LING Haibin,WU Yi,et al.Real time robust L1tracker using accelerated proximal gradient approach[C].IEEE International Conference on Computer Vision and Pattern Recognition,Providence,USA,2012:1830-1837.
[3]KWON J and LEE K M.Tracking by sampling trackers[C].International Conference on Computer Vision,Barcelona,Spain,2011:1195-1202.
[4]LIU Baiyang,HUANG Junzhou,KULIKOWAKI C,et al.Robust visual tracking using local sparse appearance model and k-selection[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(12):2968-2981.doi:10.1109/tpami.2012.215.
[5]HARE S,SAFFARI A,and TORR P H S.Struck:Structured output tracking with kernels[C].International Conference on Computer Vision,Barcelona,Spain,2011:263-270.
[6]KALAL Z,MATAS J,and MIKOLAJCZYK K.P-Nlearning:Bootstrapping binary classifiers by structural constraints[C].IEEE International Conference on Computer Vision and Pattern Recognition,San Francisco,USA,2010:49-56.
[7]BABENKO B,YANG Minghsuang,and BELONGIE S.Visual tracking with online multiple instance learning[C].IEEE International Conference on Computer Vision and Pattern Recognition,Miami,USA,2009:983-990.
[8]孙航,李晶,杜博,等.基于多阶段学习的相关滤波目标跟踪[J].电子学报,2017,45(10):2337-2342.doi:10.3969/j.issn.0372-2112.2017.10.004.SUN Hang,LI Jing,DU Bo,et al.Correlation filtering target tracking based on online multi-lifespan learning[J].Acta Electronica Sinca,2017,45(10):2337-2342.doi:10.3969/j.issn.0372-2112.2017.10.004.
[9]BOLME D S,BEVERIDGE J R,DRAPER B A,et al.Visual object tracking using adaptive correlation filters[C].IEEE International Conference on Computer Vision and Pattern Recognition,San Francisco,USA,2010:2544-2550.
[10]HENRIQUES J F,CASEIRO R,MARTINS P,et al.Exploiting the circulant structure of tracking-by-detection with kernels[C].Proceedings of European Conference on Computer Vision,Florence,Italy,2012:702-715.
[11]HENRIQUES J F,RUI C,MARTINS P,et al.High-speed tracking with kernelized correlation filters[J].IEEETransactions on Pattern Analysis&Machine Intelligence,2015,37(3):583-596.doi:10.1109/tpami.2014.2345390.
[12]DANELLJAN M,H?GER G,KHAN F,et al.Accurate scale estimation for robust visual tracking[C].Proceedings of British Machine Vision Conference,Nottingham,UK,2014:65.1-65.11.
[13]MUELLER M,SMITH N,and GHANEM B.Context-aware correlation filter tracking[C].IEEE International Conference on Conference on Computer Vision and Pattern Recognition,Honolulu,USA,2017:1387-1395.
[14]BERTINETTO L,VALMADRE J,GOLODETZ S,et al.Staple:Complementary learners for real-time tracking[C].IEEE International Conference on Conference on Computer Vision and Pattern Recognition,Las Vegas,USA,2016:1401-1409.
[15]邬战军,牛敏,许冰,等.基于谱回归特征降维与后向传播神经网络的识别方法研究[J].电子与信息学报,2016,38(4):978-984.doi:10.11999/JEIT150781.WU Zhanjun,NIU Min,XU Bing,et al.Research on recognition method based on spectral regression and back propagation neural network[J].Journal of Electronics&Information Technology,2016,38(4):978-984.doi:10.11999/JEIT150781.
[16]侯志强,张浪,余旺盛,等.基于快速傅里叶变换的局部分块视觉跟踪算法[J].电子与信息学报,2015,37(10):2397-2404.doi:10.11999/JEIT150183.HOU Zhiqiang,ZHANG Lang,YU Wangsheng,et al.Local patch tracking algorithm based on fast fourier transform[J].Journal of Electronics&Information Technology,2015,37(10):2397-2404.doi:10.11999/JEIT150183.
[17]DANELLJAN M,HAGER G,KHAN F S,et al.Discriminative scale space tracking[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2017,39(8):1561-1575.doi:10.1109/tpami.2016.2609928.
[18]熊昌镇,赵璐璐,郭芬红.自适应特征融合的核相关滤波跟踪算法[J].计算机辅助设计与图形学学报,2017,29(6):1068-1074.doi:10.3969/j.issn.1003-9775.2017.06.012.XIONG Changzhen,ZHAO Lulu,and GUO Fenhong.Kernelized correlation filters tracking based on adaptive feature fusion[J].Journal of Computer-Aided Design&Computer Graphics,2017,29(6):1068-1074.doi:10.3969/j.issn.1003-9775.2017.06.012.
[19]WU Yi,LIM J,and YANG Minghsuan.Online object tracking:a benchmark[C].Proceedings of the Computer Vision and Pattern Recognition,Portland,USA,2013:2411-2418.