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Efficient visual tracking using multi-feature regularized robust sparse coding and quantum particle filter based localization

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Abstract

Visual object tracking is a challenging task in the field of computer vision, due to many constraints like scene variation, occlusion, cluttered background and higher data size. The sparse representation tool of the compressive sensing theory, turns out to be an effective way to implement the object tracking algorithm with less computational load and at a faster speed. In this paper, we have proposed an effective object tracking framework by using a regularized robust sparse coding (RRSC) for representing the multi-feature templates of the candidate objects. Moreover, an efficient Quantum Particle Filter (QPF) based Bayesian state estimation for tracking is also proposed for localizing the target object in the subsequent frames. The RRSC assures robustness to occlusion and noise, while the QPF successfully deals with the abrupt motion of the object. The Local Binary Pattern (LBP) feature and the Ohta color feature, reconciled in appearance modelling enhance the discriminant description. Both subjective as well as objective evaluation of the proposed tracking method is carried out for validating its efficacy in comparison to the other state-of-the-art methods. The evaluation is executed on different publicly available data sets, which illustrates the superiority of the proposed method.

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References

  • Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: Computer vision and pattern recognition, 2006 IEEE Computer Society Conference on, vol 1. IEEE, pp 798–805

  • Ahn H, Lee Y-H (2016) Performance analysis of object recognition and tracking for the use of surveillance system. J Ambient Intell Humaniz Comput 7(5):673–679

    Article  Google Scholar 

  • Babenko B, Yang M-H, Belongie S (2009) Visual tracking with online multiple instance learning. In: Computer Vision and Pattern Recognition. CVPR 2009. IEEE Conference on. IEEE, pp 983–990

  • Candes EJ, Romberg JK, Tao T (2006) Stable signal recovery from incomplete and inaccurate measurements. Commun Pure Appl Math 59(8):1207–1223

    Article  MathSciNet  MATH  Google Scholar 

  • Cazzanti L, Gupta MR, Koppal AJ (2008) Generative models for similarity-based classification. Pattern Recogn 41(7):2289–2297

    Article  MATH  Google Scholar 

  • Chase BA, Geremia J (2009) Single-shot parameter estimation via continuous quantum measurement. Phys Rev A 79(2):022314

    Article  Google Scholar 

  • Davenport MA, Duarte MF, Eldar YC, Kutyniok G (2011) Introduction to compressed sensing. Preprint 93(1):2

    Google Scholar 

  • Davies E (1969) Quantum stochastic processes. Commun Math Phys 15(4):277–304

    Article  Google Scholar 

  • Donoho DL (2006) For most large underdetermined systems of linear equations the minimal 1-norm solution is also the sparsest solution. Commun Pure Appl Math 59(6):797–829

    Article  MathSciNet  MATH  Google Scholar 

  • Donoho DL, Huo X (2001) Uncertainty principles and ideal atomic decomposition. IEEE Trans Inf Theory 47(7):2845–2862

    Article  MathSciNet  MATH  Google Scholar 

  • Elad M, Aharon M (2006) Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans Image Process 15(12):3736–3745

    Article  MathSciNet  Google Scholar 

  • Gao S, Tsang IW-H, Chia L-T, Zhao P (2010) Local features are not lonely–laplacian sparse coding for image classification. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, pp 3555–3561

  • Haxby JV, Gobbini MI, Furey ML, Ishai A, Schouten JL, Pietrini P (2001) Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science 293(5539):2425–2430

  • He Z, Yi S, Cheung Y-M, You X, Tang YY (2017) Robust object tracking via key patch sparse representation. IEEE Trans Cybern 47(2):354–364

    Google Scholar 

  • Hongwei H, Ma B, Shen J (2017) Manifold regularized correlation object tracking. IEEE Trans Neural Netw Learn Syst 99:1–10

    Google Scholar 

  • Hu W, Li W, Zhang X, Maybank S (2015) Single and multiple object tracking using a multi-feature joint sparse representation. IEEE Trans Pattern Anal Mach Intell 37(4):816–833

    Article  Google Scholar 

  • Huber PJ (1973) Robust regression: asymptotics, conjectures and Monte Carlo. Ann Stat 1(5):799–821

    Article  MathSciNet  MATH  Google Scholar 

  • Jepson AD, Fleet DJ, El-Maraghi TF (2003) Robust online appearance models for visual tracking. IEEE Trans Pattern Anal Mach Intell 25(10):1296–1311

    Article  Google Scholar 

  • Kalal Z, Matas J, Mikolajczyk K (2010) Pn learning: Bootstrapping binary classifiers by structural constraints. In: Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, pp 49–56

  • Kristan M, Kovacic S, Leonardis A, Pers J (2010) A two-stage dynamic model for visual tracking. IEEE Trans Syst Man Cybern Part B (Cybern) 40(6):1505–1520

    Article  MATH  Google Scholar 

  • Kwon J, Lee KM (2010) Visual tracking decomposition. Computer Vision and Pattern Recognition (CVPR), IEEE Conference, pp 1269–1276

  • Li A, Jing Z, Hu S (2007) Robust observation model for visual tracking in particle filter. AEU Int J Electron Commun 61(3):186–194

    Article  Google Scholar 

  • Li X, He Z, You X, Chen CP (2014) A novel joint tracker based on occlusion detection. Knowl Based Syst 71:409–418

    Article  Google Scholar 

  • Li X, Liu Q, He Z, Wang H, Zhang C, Chen W-S (2016) A multi-view model for visual tracking via correlation filters. Knowl Based Syst 113:88–99

    Article  Google Scholar 

  • Liu B, Yang L, Huang J, Meer P, Gong L, Kulikowski C (2010) Robust and fast collaborative tracking with two stage sparse optimization. European conference on computer vision. Springer, New York, pp 624–637

    Google Scholar 

  • Liu Q, Ma X, Ou W, Zhou Q (2017) Visual object tracking with online sample selection via lasso regularization. Signal Image Video Process 11(5):881–888

    Article  Google Scholar 

  • Liu Q, Zhao X, Hou Z (2014) Survey of single-target visual tracking methods based on online learning. IET Comput Vision 8(5):419–428

    Article  Google Scholar 

  • Ma B, Hu H, Shen J, Zhang Y, Porikli F (2015) Linearization to nonlinear learning for visual tracking. In: Proceedings of the IEEE International Conference on Computer Vision, pp 4400–4407

  • Ma B, Huang L, Shen J, Shao L (2016a) Discriminative tracking using tensor pooling. IEEE Trans Cybern 46(11):2411–2422

    Article  Google Scholar 

  • Ma B, Huang L, Shen J, Shao L, Yang M-H, Porikli F (2016b) Visual tracking under motion blur. IEEE Trans Image Process 25(12):5867–5876

    Article  MathSciNet  Google Scholar 

  • Mairal J, Elad M, Sapiro G (2008) Sparse representation for color image restoration. IEEE Trans Image Process 17(1):53–69

    Article  MathSciNet  MATH  Google Scholar 

  • Manjunath BS, Ohm J-R, Vasudevan VV, Yamada A (2001) Color and texture descriptors. IEEE Trans Circuits Syst Video Technol 11(6):703–715

    Article  Google Scholar 

  • Mazzeo PL, Spagnolo P, Leo M, Carcagnì P, Del Coco M, Distante C (2017) Dense descriptor for visual tracking and robust update model strategy. J Ambient Intell Humaniz Comput:1–11

  • Mei X, Ling H (2009) Robust visual tracking using 1 minimization. In: Computer Vision, 2009 IEEE 12th International Conference on. IEEE, pp 1436–1443

  • Mei X, Ling H (2011) Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell 33(11):2259–2272

    Article  Google Scholar 

  • Ou W, You X, Cheung Y-M, Peng Q, Gong M, Jiang X (2012) Structured sparse coding for image representation based on l 1-graph. In: Pattern Recognition (ICPR), 2012 21st International Conference on. IEEE, pp 3220–3223

  • Ou W, You X, Tao D, Zhang P, Tang Y, Zhu Z (2014) Robust face recognition via occlusion dictionary learning. Pattern Recogn 47(4):1559–1572

    Article  Google Scholar 

  • Ou W, Yuan D, Liu Q, Cao Y (2017) Object tracking based on online representative sample selection via non-negative least square. Multimed Tools Appl. https://doi.org/10.1007/s11042-017-4672-3

  • Ramirez I, Sapiro G (2009) Sparse modelling with universal priors and learned incoherent dictionaries. Technical report, DTIC Document

    Google Scholar 

  • Ross DA, Lim J, Lin R-S, Yang M-H (2008) Incremental learning for robust visual tracking. Int J Comput Vision 77(1–3):125–141

    Article  Google Scholar 

  • Sathyanarayana S, Satzoda RK, Sathyanarayana S, Thambipillai S (2015) Vision-based patient monitoring: a comprehensive review of algorithms and technologies. J Ambient Intell Humanized Comput 1–27. https://doi.org/10.1007/s12652-015-0328-1

  • Sukumaran AN, Sankararajan R, Swaminathan M (2017) Compressed sensing based foreground detection vector for object detection in wireless visual sensor networks. AEU Int J Electron Commun 72:216–224

    Article  Google Scholar 

  • Tibshirani R (1996) Regression shrinkage and selection via the lasso. J Roy Stat Soc Ser B 58(1):267–288

    MathSciNet  MATH  Google Scholar 

  • van Handel R, Stockton JK, Mabuchi H (2005) Modelling and feedback control design for quantum state preparation. J Opt B Quantum Semiclassical Opt 7(10):S179

    Article  MathSciNet  Google Scholar 

  • Wang J, Yang J, Yu K, Lv F, Huang T, Gong Y (2010) Locality-constrained linear coding for image classification. In: Computer Vision and Pattern Recognition (CVPR), IEEE Conference, pp 3360–3367

  • Wang Q, Chen F, Xu W, Yang M-H (2012) Online discriminative object tracking with local sparse representation. In: Applications of Computer Vision (WACV), IEEE Workshop, pp 425–432

  • Wang D, Lu H, Yang M-H (2013) Online object tracking with sparse prototypes. IEEE Trans Image Process 22(1):314–325

    Article  MathSciNet  MATH  Google Scholar 

  • Wei Q, Xiong Z, Li C, Ouyang Y, Sheng H (2011) A robust approach for multiple vehicles tracking using layered particle filter. AEU Int J Electron Commun 65(7):609–618

    Article  Google Scholar 

  • Wright J, Ma Y (2010) Dense error correction via l1-minimization. IEEE Trans Inf Theory 56(7):3540–3560

    Article  MATH  Google Scholar 

  • Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  • Xingping Dong JS, Yu D (2017) Occlusion-aware real-time object tracking. IEEE Trans Multimed 19(4):763–771

    Article  Google Scholar 

  • Yang M, Zhang L (2010) Gabor feature based sparse representation for face recognition with gabor occlusion dictionary. European conference on computer vision. Springer, New York, pp 448–461

    Google Scholar 

  • Yang M, Zhang L, Yang J, Zhang D (2013) Regularized robust coding for face recognition. IEEE Trans Image Process 22(5):1753–1766

    Article  MathSciNet  MATH  Google Scholar 

  • Yu S, You X, Jiang X, Ou W, Zhu Z, Zhao Y, Chen CP, Tang Y (2015) Generalized kernel normalized mixed-norm algorithm: analysis and simulations. International Conference on Neural Information Processing. Springer, pp 61–70

    Chapter  Google Scholar 

  • Yuan X-T, Liu X, Yan S (2012) Visual classification with multitask joint sparse representation. IEEE Trans Image Process 21(10):4349–4360

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang Z, Xu Y, Yang J, Li X, Zhang D (2015) A survey of sparse representation: algorithms and applications. IEEE Access 3:490–530

    Article  Google Scholar 

  • Zhang T, Bibi A, Ghanem B (2016) In defense of sparse tracking: Circulant sparse tracker. In: Proceedings of the computer vision and pattern recognition, IEEE conference, pp 3880–3888

  • Zhao P, Yu B (2006) On model selection consistency of lasso. J Mach Learn Res 7:2541–2563

    MathSciNet  MATH  Google Scholar 

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  1. Prajna Parimita Dash

    Present address: Department of Electronics and Communication Engineering, Birla Institute of Technology, Ranchi, India

Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology, Rourkela, India

    Prajna Parimita Dash & Dipti Patra

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  1. Prajna Parimita Dash
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Correspondence to Prajna Parimita Dash.

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Dash, P.P., Patra, D. Efficient visual tracking using multi-feature regularized robust sparse coding and quantum particle filter based localization. J Ambient Intell Human Comput 10, 449–462 (2019). https://doi.org/10.1007/s12652-017-0663-5

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