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Perspective model-based visual tracking scheme for robust tracking of objects in complex environs

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Abstract

Robust tracking of moving objects is still an open problem in computer vision. The problem and its difficulty depend on many factors, which includes the availability of prior knowledge of the target. Occlusions and fast motions are among the major challenges in the process. When such hindrances occur, misjudgment of the target causes incorrect updating of target template model. Therefore, preserving the stability and precision of the tracker becomes crucial. This paper addresses the problem of handling fast moving objects and dynamic occlusions in visual tracking. The novelty of the proposed method is in the usage of an active perspective learning and a new adaptive incremental model update mechanism. A consistency measure acquired using the information from the spatial viewpoint model and the perspective model differentiates the target from its surrounding at every instance of tracking. The Discrete Hartley transform (DHT) is applied for image transformation, learning and target prediction. To auto-adjust the bounding box during tracking, a new technique of dynamic scale fill-in is used. Experimental results of the proposed method, when tested on a number of challenging datasets with occlusion, non-rigid deformation, and other major challenges highlights the better ability and robustness of the proposed method under tough conditions.

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References

  1. Agarwal S, Awan A, Roth D (2004) Learning to detect objects in images via a sparse, part-based representation. IEEE Trans Pattern Anal Mach Intell 26(11):1475–1490

    Article  Google Scholar 

  2. Amittal A, Paragios N (2004) Motion-based background subtraction using adaptive kernel density estimation. Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 I.E. Computer Society Conference on, vol. 2. IEEE

  3. Asgarizadeh M, Pourghassem H (2013) A robust object tracking synthetic structure using regional mutual information and edge correlation-based tracking algorithm in aerial surveillance application. SIViP 9:175–189

    Article  Google Scholar 

  4. Babenko B, Yang M-H, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632

    Article  Google Scholar 

  5. Barnard K, Cardei VC, Funt BV (2002) A comparison of computational color constancy algorithms - part i: methodology and experiments with synthesized data. IEEE Trans Image Process 11(9):972–983

    Article  Google Scholar 

  6. Buchsbaum G (1980) A spatial processor model for object color perception. J Franklin Inst 310:1–26

    Article  Google Scholar 

  7. Coifman B, Beymer D, McLauchlan P, Malik J (1998) Areal-time computer vision system for vehicle tracking and traffic surveillance. Transp Res C 6(4):271–288

    Article  Google Scholar 

  8. Collins RT, Liu Y, Leordeanu M (2005) Online selection of discriminative tracking features. IEEE Trans Pattern Anal Mach Intell 27(10):1631–1643

    Article  Google Scholar 

  9. Comport AI, Marchand E, Pressigout M, Chaumette F (2006) Real-time markerless tracking for augmented reality: the virtual visual servoing framework. IEEE Trans Vis Comput Graph 12(4):615–628

    Article  Google Scholar 

  10. Dyckmanns H, Matthaei R, Maurer M, Lichte B, Effertz J, Stuker D (2011) Object tracking in urban intersections based on active use of a priori knowledge: active interacting multi model filter. In: Intelligent Vehicles Symposium (IV), 2011 IEEE. IEEE, pp 625–630

  11. Elgammal A, Harwood D, Davis L (2000) Non-parametric model for background subtraction. Computer Vision—ECCV 2000. Springer, Berlin, pp 751–767

  12. Ellis L, Dowson N, Matas J, Bowden R (2011) Linear regression and adaptive appearance models for fast simultaneous modelling and tracking. Int J Comput Vis 95(2):154–179

    Article  MathSciNet  MATH  Google Scholar 

  13. Grabner H, Grabner M, Bischof H (2006) Real-time tracking via on-line boosting. BMVC, vol 1, no. 5

  14. Grabner H, Matas J, Gool L, Cattin P (2010) Tracking the invisible: learning where the object might be. In: CVPR

  15. Henriques JF, Caseiro R, Martins P, Batista J (2012) Exploiting the circulant structure of tracking-by-detection with kernels. Computer Vision–ECCV 2012. Springer, pp 702–715

  16. Ho MC, Chiang CC, Su YY (2012) Object tracking by exploiting adaptive region-wise linear subspace representations and adaptive templates in an iterative particle filter. Pattern Recogn Lett 33(5):500–512

    Article  Google Scholar 

  17. Horbert E, Rematas K, Leibe B (2011) Level-set person segmentation and tracking with multi-region appearance models and top-down shape information. International Conference on Computer Vision (ICCV). IEEE

  18. Hu W, Zhou X, Li W, Luo W, Zhang X, Maybank S (2013) Active contour-based visual tracking by integrating colors, shapes, and motions. IEEE Trans Image Process 22(5):1778–1792

    Article  MathSciNet  MATH  Google Scholar 

  19. Joy E, Peter JD (2017) Visual tracking with conditionally adaptive multiple template update scheme for intricate videos. Multimedia Systems 1–20

  20. Kim K, Chalidabhongse TH, Harwood D, Davis L (2005) Real-time foreground–background segmentation using codebook model. Real-Time Imaging 11(3):172–185

    Article  Google Scholar 

  21. Kwon J, Lee K (2010) Visual tracking decomposition. In: CVPR

  22. Lee K-C, Kriegman D (2005) Online learning of probabilistic appearance manifolds for video-based recognition and tracking. Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, vol. 1. IEEE

  23. Liu X, Qi C (2013) Future-data driven modeling of complex backgrounds using mixture of Gaussians. Neurocomputing 119:439–453

    Article  Google Scholar 

  24. Liu X, Zhao G, Yao J, Qi C (2015) Background subtraction based on low-rank and structured sparse decomposition. IEEE Trans Image Process 24(8):2502–2514

    Article  MathSciNet  Google Scholar 

  25. Malik J, Russell S (1996) Traffic surveillance and detection technology development (new traffic sensor technology). Univ. of California, Berkeley

    Google Scholar 

  26. McKenna S, Jabri S, Duric Z, Rosenfeld A, Wechsler H (2000) Tracking groups of people. Comput Vis Image Underst 80(1):42–56

    Article  MATH  Google Scholar 

  27. 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 

  28. Nigam S, Khare A (2012) Curvelet transform-based technique for tracking of moving objects. IET Comput Vis 6(3):231–251

    Article  MathSciNet  Google Scholar 

  29. Papadakis N, Bugeau A (2011) Tracking with occlusions via graph cuts. IEEE Trans Pattern Anal Mach Intell 33(1):144–157

    Article  Google Scholar 

  30. Pau CA, Barber A (1996) Traffic sensor using a color vision method. In: Proc. SPIE—Transportation Sensors and Controls: Collision Avoidance, Traffic Management, and ITS, vol 2902, pp 156–165

  31. Pérez P, Hue C, Vermaak J, Gangnet M (2002) Color-based probabilistic tracking. Proceedings of the 7th European Conference on Computer Vision, pp 661–675

  32. Polana R, Nelson R (1994) Low level recognition of human motion. In: Proc. IEEE Workshop Motion of Non-Rigid and Articulated Objects, Austin, TX, pp 77–82

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

    Article  Google Scholar 

  34. Roth D, Koller-Meier E, Van Gool L (2010) Multi-object tracking evaluated on sparse events. Multimedia Tools Appl 50:29–47 Springer Science, 2009

    Article  Google Scholar 

  35. Schiele B (2000) Model-free tracking of cars and people based on color regions. In: Proc. IEEE Int. Workshop Performance Evaluation of Tracking and Surveillance, Grenoble, France, pp 61–71

  36. Smeulders A, Chu D, Cucchiara R, Calderara S, Dehghan A, Shah M (2014) Visual tracking: an experimental survey. IEEE Trans Pattern Anal Mach Intell 36(7):1442–1468

    Article  Google Scholar 

  37. Stalder S, Grabner H, Gool L (2010) Cascaded confidence filtering for improved tracking-by-detection. In: ECCV

  38. Subudhi BN, Nanda PK, Ghosh A (2011) A change information based fast algorithm for video object detection and tracking. IEEE Tran Circuits Syst Video Technol 21(7):993–1004

    Article  Google Scholar 

  39. Sun X, Yao, H, Zhang S (2011) A novel supervised level set method for non-rigid object tracking. Computer Vision and Pattern Recognition (CVPR), 2011 I.E. Conference on. IEEE

  40. Wang D, Lu H (2014) Visual tracking via probability continuous outlier model. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognition, pp 3478–3485

  41. Weng S-K, Kuo C-M, Shu-Kang T (2006) Video object tracking using adaptive Kalman filter. J Vis Commun Image Represent 17(6):1190–1208

    Article  Google Scholar 

  42. Wu Y, Lim J, Yang M-H (2015) Object tracking benchmark. IEEE Trans Pattern Anal Mach Intell 37(9):1834–1848

    Article  Google Scholar 

  43. Yadav K, Bhattacharya J (2016) Real-time hand gesture detection and recognition for human-computer interaction. Intelligent systems technologies and applications. Springer International Publishing, pp 559–567

  44. Yang H, Shao L, Zheng F, Wang L, Song Z (2011) Recent advances and trends in visual tracking: a review. Neurocomputing 74(18):3823–3831

    Article  Google Scholar 

  45. Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38(4):1–45

    Article  Google Scholar 

  46. Yoon JH, Kim DY, Yoon KJ (2012). Visual tracking via adaptive tracker selection with multiple features. In: Proceedings of European Conference on Computer Vision (ECCV)

  47. Zeisl B, Leistner C, Saffari A, Bischof H (2010) On-line semi-supervised multiple instance boosting. In: CVPR

  48. Zhang B, Allebach JP (2008) Adaptive bilateral filter for sharpness enhancement and noise removal. IEEE Trans Image Process 17(5):664–678

    Article  MathSciNet  Google Scholar 

  49. Zhang L, van der Maaten L (2014) Preserving structure in model-free tracking. IEEE Trans Pattern Anal Mach Intell 36(4):756–769

    Article  Google Scholar 

  50. Zhang T, Ghanem B, Liu S, Ahuja N (2013) Robust visual tracking via structured multi-task sparse learning. Int J Comput Vis 101(2):367–383

    Article  MathSciNet  Google Scholar 

  51. Zhang T, Liu S, Ahuja N, Yang MH, Ghanem B (2015) Robust visual tracking via consistent low-rank sparse learning. Int J Comput Vis 111(2):171–190

    Article  Google Scholar 

  52. Zhang T et al (2016) Robust visual tracking via exclusive context modeling. IEEE Transactions on Cybernetics 46(1):51–63

    Article  Google Scholar 

  53. Zhong W, Lu H, Yang M-H (2014) Robust object tracking via sparse collaborative appearance model. IEEE Trans Image Process 23(5):2356–2368

    Article  MathSciNet  MATH  Google Scholar 

  54. Zhou SK, Chellappa R, Moghaddam B (2004) Visual tracking and recognition using appearance-adaptive models in particle filters. IEEE Trans Image Process 13(11):1491–1506

    Article  Google Scholar 

  55. Zhou X, Yang C, Yu W (2013) Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(3):597–610

    Article  Google Scholar 

  56. Zimmermann K, Matas J, Svoboda T (2009) Tracking by an optimal sequence of linear predictors. IEEE Trans Pattern Anal Mach Intell 31(4):677–692

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Technology, Karunya University, Coimbatore, India

    Emmanuel Joy & J. Dinesh Peter

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  1. Emmanuel Joy
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  2. J. Dinesh Peter
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Correspondence to Emmanuel Joy.

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Joy, E., Peter, J.D. Perspective model-based visual tracking scheme for robust tracking of objects in complex environs. Multimed Tools Appl 77, 19745–19768 (2018). https://doi.org/10.1007/s11042-017-5424-0

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