基于高置信度更新策略的高速相关滤波跟踪算法
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摘要
为了满足在线目标跟踪算法的实时性需求并提高算法的稳健性,提出一种基于高置信度更新策略的相关滤波跟踪算法。在目标区域提取、融合多特征,以构建稳健的外观表达,并利用投影矩阵对特征进行降维,以提高算法的运行效率;通过相关滤波器寻找最大响应值,从而快速定位目标;利用最大响应值和平均峰值相关能量指标,设计了一种高置信度更新策略。结果表明:所提算法在大规模公开数据集上取得了较高的跟踪精度和成功率,平均跟踪速度达到122.3 frame/s。
To satisfy the real-time requirements of the online object tracking algorithm and improve the robustness of the algorithm, we propose a correlation filter-based tracking algorithm with high-confidence updating strategy. Multi-features are extracted and integrated in the target region to construct robust appearance representation, and the projection matrix for dimension reduction of features is used to improve the operational efficiency of the algorithm. The correlation filter is used to localize the target at a high speed via the maximum response value. Two indicators of maximum response value and average peak-to-correlation energy are utilized to design a high-confidence updating strategy. The results show that the proposed algorithm achieves high tracking precision and success rate on large-scale public datasets while running at 122.3 frame/s on average.
To satisfy the real-time requirements of the online object tracking algorithm and improve the robustness of the algorithm, we propose a correlation filter-based tracking algorithm with high-confidence updating strategy. Multi-features are extracted and integrated in the target region to construct robust appearance representation, and the projection matrix for dimension reduction of features is used to improve the operational efficiency of the algorithm. The correlation filter is used to localize the target at a high speed via the maximum response value. Two indicators of maximum response value and average peak-to-correlation energy are utilized to design a high-confidence updating strategy. The results show that the proposed algorithm achieves high tracking precision and success rate on large-scale public datasets while running at 122.3 frame/s on average.
引文
[1] Lu H C,Li P X,Wang D.Visual object tracking:a survey[J].Pattern Recognition and Artificial Intelligence,2018,31(1):61-76.卢湖川,李佩霞,王栋.目标跟踪算法综述[J].模式识别与人工智能,2018,31(1):61-76.
[2] Kristan M,Leonardis A,Matas J,et al.The visual object tracking VOT2017 challenge results[C]//Proceedings of IEEE International Conference on Computer Vision Workshop,October 22-29,2017,Venice,Italy.New York:IEEE,2017:1949-1972.
[3] Bolme D S,Beveridge J R,Draper B A,et al.Visual object tracking using adaptive correlation filters[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 13-18,2010,San Francisco,CA,USA.New York:IEEE,2010:2544-2550.
[4] Henriques J F,Caseiro R,Martins P,et al.Exploiting the circulant structure of tracking-by-detection with kernels[C]//Proceedings of the 12th European Conference on Computer Vision,October 7-13,2012,Florence,Italy.Heidelberg:Springer-Verlag,2012:702-715.
[5] Henriques J F,Caseiro R,Martins P,et al.High-speed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):583-596.
[6] Danelljan M,Khan F S,Felsberg M,et al.Adaptive color attributes for real-time visual tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 23-28,2014,Columbus,OH,USA.New York:IEEE,2014:1090-1097.
[7] Shen Q,Yan X L,Liu L F,et al.Multi-scale correlation filtering tracker based on adaptive feature selection[J].Acta Optica Sinica,2017,37(5):0515001.沈秋,严小乐,刘霖枫,等.基于自适应特征选择的多尺度相关滤波跟踪[J].光学学报,2017,37(5):0515001.
[8] Li Y,Zhu J K.A scale adaptive kernel correlation filter tracker with feature integration[C]//Proceedings of the 13th European Conference on Computer Vision,September 6-7,12,2014,Zurich,Switzerland.Switzerland:Springer,2014:254-265.
[9] Danelljan M,H?ger G,Khan F S,et al.Accurate scale estimation for robust visual tracking[C]//Proceedings of British Machine Vision Conference,September 1-5,2014,Nottingham,UK.Durham:BMVA Press,2014:1-11.
[10] Danelljan M,Hager G,Khan F S,et al.Discriminative scale space tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(8):1561-1575.
[11] Liao X F,Hou Z Q,Yu W S,et al.A scale adapted tracking algorithm based on kernelized correlation[J].Acta Optica Sinica,2018,38(7):0715002.廖秀峰,侯志强,余旺盛,等.基于核相关的尺度自适应视觉跟踪[J].光学学报,2018,38(7):0715002.
[12] Zhang T Z,Xu C S,Yang M H.Multi-task correlation particle filter for robust object tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,July 21-26,2017,Honolulu,HI,USA.New York:IEEE,2017:4819-4827.
[13] Wang M M,Liu Y,Huang Z Y.Large margin object tracking with circulant feature maps[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,July 21-26,2017,Honolulu,HI,USA.New York:IEEE,2017:4800-4808.
[14] Ma X H.Updating method of improved gradient threshold in object tracking[J].Laser & Optoelectronics Progress,2018,55(6):061502.马晓虹.目标跟踪中增强梯度阈值的更新方法[J].激光与光电子学进展,2018,55(6):061502.
[15] Ma C,Yang X K,Zhang C Y,et al.Long-term correlation tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 7-12,2015,Boston,MA,USA.New York:IEEE,2015:5388-5396.
[16] Ma C,Huang J B,Yang X K,et al.Hierarchical convolutional features for visual tracking[C]//Proceedings of IEEE International Conference on Computer Vision,December 7-13,2015,Santiago,Chile.New York:IEEE,2015:3074-3082.
[17] Danelljan M,Robinson A,Khan F S,et al.Beyond correlation filters:learning continuous convolution operators for visual tracking[C]//Proceedings of the 14th European Conference on Computer Vision,October 11-14,2016,Amsterdam,The Netherlands.Switzerland:Springer,2016:472-488.
[18] Wang X,Hou Z Q,Yu W S,et al.Target scale adaptive robust tracking based on fusion of multilayer convolutional features[J].Acta Optica Sinica,2017,37(11):1115005.王鑫,侯志强,余旺盛,等.基于多层卷积特征融合的目标尺度自适应稳健跟踪[J].光学学报,2017,37(11):1115005.
[19] Danelljan M,Bhat G,Khan F S,et al.ECO:efficient convolution operators for tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,July 21-26,2017,Honolulu,HI,USA.New York:IEEE,2017:6931-6939.
[20] Xiong C Z,Che M Q,Wang R L,et al.Robust and real-time visual tracking via dual model adaptive switching[J].Acta Optica Sinica,2018,38(10):1015002.熊昌镇,车满强,王润玲,等.稳健的双模型自适应切换实时跟踪算法[J].光学学报,2018,38(10):1015002.
[21] Nam H,Han B.Learning multi-domain convolutional neural networks for visual tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 27-30,2016,Las Vegas,NV,USA.New York:IEEE,2016:4293-4302.
[22] Bertinetto L,Valmadre J,Henriques J F,et al.Fully-convolutional siamese networks for object tracking[C]//Proceedings of the 14th European Conference on Computer Vision,October 11-14,2016,Amsterdam,The Netherlands.Switzerland:Springer,2016:850-865.
[23] Gao J Y,Zhang T Z,Yang X S,et al.Deep relative tracking[J].IEEE Transactions on Image Processing,2017,26(4):1845-1858.
[24] Sun C,Wang D,Lu H C,et al.Learning spatial-aware regressions for visual tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 18-23,2018,Salt Lake City,UT,USA.New York:IEEE,2018:8962-8970.
[25] Wu Y,Lim J,Yang M H.Object tracking benchmark[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1834-1848.
[26] Galoogahi H K,Sim T,Lucey S.Correlation filters with limited boundaries[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 7-12,2015,Boston,MA,USA.New York:IEEE,2015:4630-4638.
[27] Gundogdu E,Alatan A A.Spatial windowing for correlation filter based visual tracking[C]//Proceedings of IEEE International Conference on Image Processing,September 25-28,2016,Phoenix,AZ,USA.New York:IEEE,2016:1684-1688.
[28] Guan H,Xue X Y,An Z Y.Advances on application of deep learning for video object tracking[J].Acta Automatica Sinica,2016,42(6):834-847.管皓,薛向阳,安志勇.深度学习在视频目标跟踪中的应用进展与展望[J].自动化学报,2016,42(6):834-847.
[2] Kristan M,Leonardis A,Matas J,et al.The visual object tracking VOT2017 challenge results[C]//Proceedings of IEEE International Conference on Computer Vision Workshop,October 22-29,2017,Venice,Italy.New York:IEEE,2017:1949-1972.
[3] Bolme D S,Beveridge J R,Draper B A,et al.Visual object tracking using adaptive correlation filters[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 13-18,2010,San Francisco,CA,USA.New York:IEEE,2010:2544-2550.
[4] Henriques J F,Caseiro R,Martins P,et al.Exploiting the circulant structure of tracking-by-detection with kernels[C]//Proceedings of the 12th European Conference on Computer Vision,October 7-13,2012,Florence,Italy.Heidelberg:Springer-Verlag,2012:702-715.
[5] Henriques J F,Caseiro R,Martins P,et al.High-speed tracking with kernelized correlation filters[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(3):583-596.
[6] Danelljan M,Khan F S,Felsberg M,et al.Adaptive color attributes for real-time visual tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 23-28,2014,Columbus,OH,USA.New York:IEEE,2014:1090-1097.
[7] Shen Q,Yan X L,Liu L F,et al.Multi-scale correlation filtering tracker based on adaptive feature selection[J].Acta Optica Sinica,2017,37(5):0515001.沈秋,严小乐,刘霖枫,等.基于自适应特征选择的多尺度相关滤波跟踪[J].光学学报,2017,37(5):0515001.
[8] Li Y,Zhu J K.A scale adaptive kernel correlation filter tracker with feature integration[C]//Proceedings of the 13th European Conference on Computer Vision,September 6-7,12,2014,Zurich,Switzerland.Switzerland:Springer,2014:254-265.
[9] Danelljan M,H?ger G,Khan F S,et al.Accurate scale estimation for robust visual tracking[C]//Proceedings of British Machine Vision Conference,September 1-5,2014,Nottingham,UK.Durham:BMVA Press,2014:1-11.
[10] Danelljan M,Hager G,Khan F S,et al.Discriminative scale space tracking[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017,39(8):1561-1575.
[11] Liao X F,Hou Z Q,Yu W S,et al.A scale adapted tracking algorithm based on kernelized correlation[J].Acta Optica Sinica,2018,38(7):0715002.廖秀峰,侯志强,余旺盛,等.基于核相关的尺度自适应视觉跟踪[J].光学学报,2018,38(7):0715002.
[12] Zhang T Z,Xu C S,Yang M H.Multi-task correlation particle filter for robust object tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,July 21-26,2017,Honolulu,HI,USA.New York:IEEE,2017:4819-4827.
[13] Wang M M,Liu Y,Huang Z Y.Large margin object tracking with circulant feature maps[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,July 21-26,2017,Honolulu,HI,USA.New York:IEEE,2017:4800-4808.
[14] Ma X H.Updating method of improved gradient threshold in object tracking[J].Laser & Optoelectronics Progress,2018,55(6):061502.马晓虹.目标跟踪中增强梯度阈值的更新方法[J].激光与光电子学进展,2018,55(6):061502.
[15] Ma C,Yang X K,Zhang C Y,et al.Long-term correlation tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 7-12,2015,Boston,MA,USA.New York:IEEE,2015:5388-5396.
[16] Ma C,Huang J B,Yang X K,et al.Hierarchical convolutional features for visual tracking[C]//Proceedings of IEEE International Conference on Computer Vision,December 7-13,2015,Santiago,Chile.New York:IEEE,2015:3074-3082.
[17] Danelljan M,Robinson A,Khan F S,et al.Beyond correlation filters:learning continuous convolution operators for visual tracking[C]//Proceedings of the 14th European Conference on Computer Vision,October 11-14,2016,Amsterdam,The Netherlands.Switzerland:Springer,2016:472-488.
[18] Wang X,Hou Z Q,Yu W S,et al.Target scale adaptive robust tracking based on fusion of multilayer convolutional features[J].Acta Optica Sinica,2017,37(11):1115005.王鑫,侯志强,余旺盛,等.基于多层卷积特征融合的目标尺度自适应稳健跟踪[J].光学学报,2017,37(11):1115005.
[19] Danelljan M,Bhat G,Khan F S,et al.ECO:efficient convolution operators for tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,July 21-26,2017,Honolulu,HI,USA.New York:IEEE,2017:6931-6939.
[20] Xiong C Z,Che M Q,Wang R L,et al.Robust and real-time visual tracking via dual model adaptive switching[J].Acta Optica Sinica,2018,38(10):1015002.熊昌镇,车满强,王润玲,等.稳健的双模型自适应切换实时跟踪算法[J].光学学报,2018,38(10):1015002.
[21] Nam H,Han B.Learning multi-domain convolutional neural networks for visual tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 27-30,2016,Las Vegas,NV,USA.New York:IEEE,2016:4293-4302.
[22] Bertinetto L,Valmadre J,Henriques J F,et al.Fully-convolutional siamese networks for object tracking[C]//Proceedings of the 14th European Conference on Computer Vision,October 11-14,2016,Amsterdam,The Netherlands.Switzerland:Springer,2016:850-865.
[23] Gao J Y,Zhang T Z,Yang X S,et al.Deep relative tracking[J].IEEE Transactions on Image Processing,2017,26(4):1845-1858.
[24] Sun C,Wang D,Lu H C,et al.Learning spatial-aware regressions for visual tracking[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 18-23,2018,Salt Lake City,UT,USA.New York:IEEE,2018:8962-8970.
[25] Wu Y,Lim J,Yang M H.Object tracking benchmark[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1834-1848.
[26] Galoogahi H K,Sim T,Lucey S.Correlation filters with limited boundaries[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition,June 7-12,2015,Boston,MA,USA.New York:IEEE,2015:4630-4638.
[27] Gundogdu E,Alatan A A.Spatial windowing for correlation filter based visual tracking[C]//Proceedings of IEEE International Conference on Image Processing,September 25-28,2016,Phoenix,AZ,USA.New York:IEEE,2016:1684-1688.
[28] Guan H,Xue X Y,An Z Y.Advances on application of deep learning for video object tracking[J].Acta Automatica Sinica,2016,42(6):834-847.管皓,薛向阳,安志勇.深度学习在视频目标跟踪中的应用进展与展望[J].自动化学报,2016,42(6):834-847.