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In recent years,sparse representation theory has acquired considerable progress and has extensively been used in visual tracking.Most trackers used the sparse coefficients to merely calculate the position of the target according to the reconstruction error relative to sparse coefficients,and often neglected the information contained by representation residual matrix in representing step.Consequently,we present a novel sparse representation based tracker which takes representation residual matrix into consideration.First of all,at initialization of a new frame,we reconstruct the frame by singular value decomposition (SVD) to eliminate noise and useless information,which contributes a friendly frame for the following representation step.To obtain the compact representation of the target,we build L2-norm regularization according to the distance between the candidates wrapped in particle framework and the reconstruction calculated by dictionary templates and residual matrix.Additionally,we use the L1-norm constraint to restrict the sparse coefficients and the residual matrix of each candidate.Secondly,as the built optimization problem does not have closed-form solution,we design a method to compute the coefficients and the residual matrix iteratively.During each iteration,the coefficients are obtained by solving classical least absolute shrinkage and selectionator operator (LASSO) model,and the residual matrix is achieved by shrinkage operation.After solving the optimization problem,we compute the score of each candidate for evaluating the truth target with considering coefficients and residual matrix.The score is formulated as weighted reconstruction error which consists of dictionary templates,candidates,coefficients and residual matrix. The weight is the exponential value of absolute value of elements in residual matrix.Finally,for capturing the varying appearance of target in series,we update the dictionary template with assembled template,which is composed of residual matrix,selected candidate and dictionary template.In this paper,the template to be replaced is determined according to the score which is inversely proportional to the distance between the selected candidate and each dictionary template. Then we update the dictionary frame by frame during tracking process.Contributions of this work are threefold:1) the representation model captures holistic and local features of target and makes the tracker robust to varying illumination, shape transformation,and background clutter,profiting from preprocessing of SVD reconstruction,the model exhibits a more compact representation of target without disturbance of noisy variance;2) we employ a weight matrix to adjust reconstruction error in candidate evaluation step,as described above,the weight matrix strengthens the effect of error in residual matrix for evaluating candidates from which target is selected,it is noted that weights are all greater than one,which leads to reconstruction error expanding according to the error value of residual matrix,and keeps pixels where there is small error value believable for evaluation;and 3) we adopt an assembled template to update dictionary template and reconstruction of coefficients of selected candidate,which alleviates dictionary degradation and tracking drift problems and provides an accurate description of new appearance of target.In order to illustrate the performance of the proposed tracker,we enforce the algorithm on several challenging sequences and compare the proposed algorithm with five state-of-art methods,whose codes are all supplied by the authors.For complete illustration,both qualitative evaluation and quantitative evaluation are presented in experiment section.Through the experimental results,we could conclude that the proposed algorithm has a more favorable and robust performance than other state-of-art algorithms when dealing with kinds of situations during tracking.
[1] Gao W, Tang Y, Zhu M 2015 Acta Phys. Sin. 64 014205 (in Chinese) [高文, 汤洋, 朱明2015物理学报64 014205]
[2] Xu Y, Zhang B, Zhong Z F 2015 Pattern Recogn. Lett. 68 9
[3] Fan Q, Qi C 2016 Neurocomputing 175 81
[4] Kim M, Han D K, Ko H 2016 Information Fusion 27 198
[5] Mei X, Ling H B 2009 Proceedings of IEEE International Conference on Computer Vision Kyoto, Japan, September 27-October 4, 2009 p1436
[6] Liu B Y, Huang J Z, Yang L, Kulikowsk C 2011 Proceedings of IEEE Computer Vision, Pattern Recognition Colorado, Springs, June 21-252011 p1313
[7] Jia X, Lu H C, Yang M H 2012 Proceedings of IEEE Computer Vision, Pattern Recognition Providence, Rhode Island, June 16-21, 2012 p1822
[8] Liu H P, Sun F C 2010 Proceedings of International Conference on Pattern Recognition Istanbul, Turkey, August 23-26, 2010 p1702
[9] Wang B X, Zhao B J, Tang L B, Wang S G, Wu J H 2014 Acta Phys. Sin. 63 234201 (in Chinese) [王保宪, 赵保军, 唐林波, 王水根, 吴京辉2014物理学报63 234201]
[10] Liu B Y, Yang L, Huang J Z, Meer P, Gong L G, Kulikowski C 2010 Proceedings of the 11th European Conference on Computer Vision Crete, Greece, September 5-11, 2010 p624
[11] Wang Q, Chen F, Xu W L, Yang M H 2012 Proceedings of I EE E Workshop on Applications of Computer Vision Breckenridge, C O, January 9-11, 2012 p425
[12] Bao C L, Wu Y, Ling H B, Ji H 2012 Proceedings of IEEE Computer Vision, Pattern Recognition Providence, Rhode Island, June 16-21, 2012 p1830
[13] Pérez P, Hue C, Vermaak J, Gangnet M 2002 European Conference on Computer Vision Copenhagen, Denmark, May 28-31, 2002 p661
[14] Zhang T Z, Ghanem B, Liu S, Ahuja N 2013 Int. J. Comput. Vision 101 367
[15] Zhuang B H, Lu H C, Xiao Z Y, Wang D 2014 IEEE Trans. Image Proces. 23 1872
[16] Zhong W, Lu H C, Yang M H 2012 Proceedings of IEEE Computer Vision, Pattern Recognition Providence, Rhode Island, June 16-21, 2012 p1838
[17] Donoho D L 2006 IEEE Trans. Inform. Theory 52 1289
[18] Donoho D L, T SA IG Y 2006 Signal Proces. 86 533
[19] Rao S R, Tron R, Vidal R, Ma Y 2009 IEEE Trans. PAMI. 32 1832
[20] Wang D, Lu H C 2012 IEEE Signal Proces. Lett. 19 711
[21] Yan H, Yang J 2016 Neurocomputing 173 1936
[22] Efron B, Hastie T, Johnstone I, Tibshirani R 2004 Ann. Statist. 32 407
[23] Hale E T, Yin W, Zhang Y 2008 SIAM J. Opt. 19 1107
[24] Wu Y, Lim J, Yang M H 2013 Proceedings of IEEE Computer Vision, Pattern Recognition Portland, Oregon, June 23-28, 2013 p2411
[25] Ross D, Lim J, Lin R, Yang M H 2008 Int. J. Comput. Vision 77 125
[26] Kalal Z, Mikolajczyk K, Matas J 2012 IEEE Trans. on PAMI 34 1409
[27] Everingham M, Gool L V, Williams C K I, Winn J M, Zisserman A 2010 Int. J. Comput. Vision 88 303
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[1] Gao W, Tang Y, Zhu M 2015 Acta Phys. Sin. 64 014205 (in Chinese) [高文, 汤洋, 朱明2015物理学报64 014205]
[2] Xu Y, Zhang B, Zhong Z F 2015 Pattern Recogn. Lett. 68 9
[3] Fan Q, Qi C 2016 Neurocomputing 175 81
[4] Kim M, Han D K, Ko H 2016 Information Fusion 27 198
[5] Mei X, Ling H B 2009 Proceedings of IEEE International Conference on Computer Vision Kyoto, Japan, September 27-October 4, 2009 p1436
[6] Liu B Y, Huang J Z, Yang L, Kulikowsk C 2011 Proceedings of IEEE Computer Vision, Pattern Recognition Colorado, Springs, June 21-252011 p1313
[7] Jia X, Lu H C, Yang M H 2012 Proceedings of IEEE Computer Vision, Pattern Recognition Providence, Rhode Island, June 16-21, 2012 p1822
[8] Liu H P, Sun F C 2010 Proceedings of International Conference on Pattern Recognition Istanbul, Turkey, August 23-26, 2010 p1702
[9] Wang B X, Zhao B J, Tang L B, Wang S G, Wu J H 2014 Acta Phys. Sin. 63 234201 (in Chinese) [王保宪, 赵保军, 唐林波, 王水根, 吴京辉2014物理学报63 234201]
[10] Liu B Y, Yang L, Huang J Z, Meer P, Gong L G, Kulikowski C 2010 Proceedings of the 11th European Conference on Computer Vision Crete, Greece, September 5-11, 2010 p624
[11] Wang Q, Chen F, Xu W L, Yang M H 2012 Proceedings of I EE E Workshop on Applications of Computer Vision Breckenridge, C O, January 9-11, 2012 p425
[12] Bao C L, Wu Y, Ling H B, Ji H 2012 Proceedings of IEEE Computer Vision, Pattern Recognition Providence, Rhode Island, June 16-21, 2012 p1830
[13] Pérez P, Hue C, Vermaak J, Gangnet M 2002 European Conference on Computer Vision Copenhagen, Denmark, May 28-31, 2002 p661
[14] Zhang T Z, Ghanem B, Liu S, Ahuja N 2013 Int. J. Comput. Vision 101 367
[15] Zhuang B H, Lu H C, Xiao Z Y, Wang D 2014 IEEE Trans. Image Proces. 23 1872
[16] Zhong W, Lu H C, Yang M H 2012 Proceedings of IEEE Computer Vision, Pattern Recognition Providence, Rhode Island, June 16-21, 2012 p1838
[17] Donoho D L 2006 IEEE Trans. Inform. Theory 52 1289
[18] Donoho D L, T SA IG Y 2006 Signal Proces. 86 533
[19] Rao S R, Tron R, Vidal R, Ma Y 2009 IEEE Trans. PAMI. 32 1832
[20] Wang D, Lu H C 2012 IEEE Signal Proces. Lett. 19 711
[21] Yan H, Yang J 2016 Neurocomputing 173 1936
[22] Efron B, Hastie T, Johnstone I, Tibshirani R 2004 Ann. Statist. 32 407
[23] Hale E T, Yin W, Zhang Y 2008 SIAM J. Opt. 19 1107
[24] Wu Y, Lim J, Yang M H 2013 Proceedings of IEEE Computer Vision, Pattern Recognition Portland, Oregon, June 23-28, 2013 p2411
[25] Ross D, Lim J, Lin R, Yang M H 2008 Int. J. Comput. Vision 77 125
[26] Kalal Z, Mikolajczyk K, Matas J 2012 IEEE Trans. on PAMI 34 1409
[27] Everingham M, Gool L V, Williams C K I, Winn J M, Zisserman A 2010 Int. J. Comput. Vision 88 303
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