Robust object tracking based on ridge regression and multi-scale local sparse coding

Recently, the technology of visual object tracking has achieved great success. However, it is still extraordinary challenging for some factors, such as scale variations, partial occlusions and so on. To deal with the problem of scale variations of the target, this paper proposes a hybrid tracking algorithm based on ridge regression and multi-scale local sparse coding. The hybrid tracking algorithm contains three parts. Firstly, a discriminative model based on two ridge regression models which include a correlation filtering ridge regression model and a color statistics ridge regression model, is used to estimate the approximate position of the target. Secondly, a multi-scale local sparse coding with particle filtering model, which combines local overlapped patches and local non-overlapped patches, is used to estimate the precise position and scale variations of the target. Thirdly, the appearance model of the target in the discriminative model based on ridge regression is updated according to the precise position and scale variations of the target in the second part. At the end, extensive experiments verify the effectiveness of the hybrid tracking algorithm in dealing with scale variations of the target.

[1]  Jiri Matas,et al.  Discriminative Correlation Filter with Channel and Spatial Reliability , 2017, CVPR.

[2]  Chen Wang,et al.  Kernel Cross-Correlator , 2017, AAAI.

[3]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Junzhou Huang,et al.  Robust tracking using local sparse appearance model and K-selection , 2011, CVPR 2011.

[5]  Ming-Hsuan Yang,et al.  Incremental Learning for Robust Visual Tracking , 2008, International Journal of Computer Vision.

[6]  Zhe Chen,et al.  MUlti-Store Tracker (MUSTer): A cognitive psychology inspired approach to object tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Ling Shao,et al.  Visual Tracking Using Strong Classifier and Structural Local Sparse Descriptors , 2015, IEEE Transactions on Multimedia.

[8]  Bernard Ghanem,et al.  Context-Aware Correlation Filter Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Jiri Matas,et al.  Robust scale-adaptive mean-shift for tracking , 2013, Pattern Recognit. Lett..

[10]  Michael Felsberg,et al.  Adaptive Color Attributes for Real-Time Visual Tracking , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Michael Felsberg,et al.  ECO: Efficient Convolution Operators for Tracking , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[12]  Bernard Ghanem,et al.  A Benchmark and Simulator for UAV Tracking , 2016, ECCV.

[13]  Bohyung Han,et al.  Learning Multi-domain Convolutional Neural Networks for Visual Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Hamid R. Rabiee,et al.  Patchwise Joint Sparse Tracking With Occlusion Detection , 2014, IEEE Transactions on Image Processing.

[15]  Gang Wang,et al.  Real-time part-based visual tracking via adaptive correlation filters , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Ming-Hsuan Yang,et al.  Long-term correlation tracking , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  Ming-Hsuan Yang,et al.  Object Tracking Benchmark , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Lei Zhang,et al.  Robust Online Matrix Factorization for Dynamic Background Subtraction , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Luca Bertinetto,et al.  Staple: Complementary Learners for Real-Time Tracking , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[20]  Huchuan Lu,et al.  Visual Tracking via Probability Continuous Outlier Model , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Yong Liu,et al.  Large Margin Object Tracking with Circulant Feature Maps , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Huchuan Lu,et al.  Robust object tracking via sparsity-based collaborative model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Huchuan Lu,et al.  Object tracking with L2-RLS , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[24]  Zdenek Kalal,et al.  Tracking-Learning-Detection , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Guillermo Sapiro,et al.  Online Learning for Matrix Factorization and Sparse Coding , 2009, J. Mach. Learn. Res..

[26]  Erik Blasch,et al.  Encoding color information for visual tracking: Algorithms and benchmark , 2015, IEEE Transactions on Image Processing.

[27]  Huchuan Lu,et al.  Robust Object Tracking via Sparse Collaborative Appearance Model , 2014, IEEE Transactions on Image Processing.

[28]  Stan Sclaroff,et al.  MEEM: Robust Tracking via Multiple Experts Using Entropy Minimization , 2014, ECCV.

[29]  Michael Felsberg,et al.  Accurate Scale Estimation for Robust Visual Tracking , 2014, BMVC.

[30]  Jianke Zhu,et al.  A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration , 2014, ECCV Workshops.

[31]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[32]  Michael Felsberg,et al.  Learning Spatially Regularized Correlation Filters for Visual Tracking , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[33]  Peng Chen,et al.  Multi-scale patch-based sparse appearance model for robust object tracking , 2014, Machine Vision and Applications.

[34]  Lei Zhang,et al.  Learning Support Correlation Filters for Visual Tracking , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Michael Felsberg,et al.  Discriminative Scale Space Tracking , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Huchuan Lu,et al.  Tri-Tracking: Combining Three Independent Views for Robust Visual Tracking , 2012, Int. J. Image Graph..

[37]  Bruce A. Draper,et al.  Visual object tracking using adaptive correlation filters , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[38]  Philip H. S. Torr,et al.  Struck: Structured output tracking with kernels , 2011, ICCV.

[39]  Rui Caseiro,et al.  Exploiting the Circulant Structure of Tracking-by-Detection with Kernels , 2012, ECCV.

[40]  Ping Feng,et al.  Dual-scale structural local sparse appearance model for robust object tracking , 2017, Neurocomputing.

[41]  Changsheng Xu,et al.  Multi-task Correlation Particle Filter for Robust Object Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[42]  Li Bai,et al.  Minimum error bounded efficient ℓ1 tracker with occlusion detection , 2011, CVPR 2011.

[43]  Lei Zhang,et al.  Object Tracking via Dual Linear Structured SVM and Explicit Feature Map , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[44]  Huchuan Lu,et al.  Visual tracking via adaptive structural local sparse appearance model , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[45]  Huchuan Lu,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON IMAGE PROCESSING 1 Online Object Tracking with Sparse Prototypes , 2022 .

[46]  Ping Feng,et al.  A hybrid tracking framework based on kernel correlation filtering and particle filtering , 2018, Neurocomputing.