Efficient visual tracking via sparse representation and back-projection histogram

  • Oumaima SlitiEmail author
  • Habib Hamam


Sparse modeling has been successfully applied in object tracking methods. When the algorithms lose track of the target, it usually keeps locating a part of the background or starts locating another different object, which has a similar appearance to the original one. In this paper, we present a novel-tracking algorithm based on sparse representation and back-projection technique for feature and region extraction. We address the issue of the tracking by modeling the target appearance using the sparse approximation, thereafter, we apply a back-projection process to identify its region. We exploit the spatial information by back-projecting the sparse coefficient of the template in each frame. Thereby, we guarantee a more robust localization of the target as we handle the foreground/background separation. Our tracker proved to be more stable and less prone to drift away.


Sparse coding Back-projection Tracking 



  1. 1.
    Adam A, Rivlin E, Shimshoni I (2006) Robust fragments-based tracking using the integral histogram. In: 2006 IEEE computer society conference on computer vision and pattern recognition, vol 1. IEEE, pp 798–805Google Scholar
  2. 2.
    Aharon M, Elad M, Bruckstein A et al (2006) K-svd: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311zbMATHCrossRefGoogle Scholar
  3. 3.
    Ahmed N, Natarajan T, Rao KR (1974) Discrete cosine transform. IEEE Trans Comput 100(1):90–93MathSciNetzbMATHCrossRefGoogle Scholar
  4. 4.
    Aliabadian A, Akbarpour E, Yosefi M (2012) Kernel based approach toward automatic object detection and tracking in surveillance systems. Int J Soft Comput Eng 2:82–87Google Scholar
  5. 5.
    Almazan J, Gajic B, Murray N, Larlus D (2018) Re-id done right: towards good practices for person re-identification. arXiv preprint arXiv:1801.05339
  6. 6.
    Babenko B, Yang MH, Belongie S (2011) Robust object tracking with online multiple instance learning. IEEE Trans Pattern Anal Mach Intell 33(8):1619–1632CrossRefGoogle Scholar
  7. 7.
    Babu RV, Pérez P, Bouthemy P (2007) Robust tracking with motion estimation and local kernel-based color modeling. Image Vis Comput 25(8):1205–1216CrossRefGoogle Scholar
  8. 8.
    Bruckstein AM, Donoho DL, Elad M (2009) From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev 51(1):34–81MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Brumitt B, Meyers B, Krumm J, Kern A, Shafer S (2000) Multi-camera multi-person tracking for easyliving. In: Conference. Proceedings of the 3rd IEEE international workshop on visual surveillanceGoogle Scholar
  10. 10.
    Brutzer S, Höferlin B, Heidemann G (2011) Evaluation of background subtraction techniques for video surveillance. In: 2011 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1937–1944Google Scholar
  11. 11.
    Comaniciu D, Ramesh V (2003) Real-time tracking of non-rigid objects using mean shift. US Patent 6,590,999Google Scholar
  12. 12.
    Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. In: IEEE conference on computer vision and pattern recognition, 2000. Proceedings, vol 2. IEEE, pp 142–149Google Scholar
  13. 13.
    Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577CrossRefGoogle Scholar
  14. 14.
    Davis G, Mallat S, Avellaneda M (1997) Adaptive greedy approximations. Constr Approx 13(1):57–98MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Du D, Qi H, Huang Q, Zeng W, Zhang C (2013) Abnormal event detection in crowded scenes based on structural multi-scale motion interrelated patterns. In: 2013 IEEE international conference on multimedia and expo (ICME). IEEE, pp 1–6Google Scholar
  16. 16.
    Engan K, Aase SO, Husoy JH (1999) Method of optimal directions for frame design. In: 1999 IEEE international conference on acoustics, speech, and signal processing, 1999. Proceedings, vol 5. IEEE, pp 2443–2446Google Scholar
  17. 17.
    Engan K, Aase SO, Husøy JH (2000) Multi-frame compression: theory and design. Signal Process 80(10):2121–2140zbMATHCrossRefGoogle Scholar
  18. 18.
    Grabner H, Grabner M, Bischof H (2006) Real-time tracking via on-line boosting. In: BMVC, vol 1, p 6Google Scholar
  19. 19.
    Gupta M, Sastry RCR, Chintalapoodi P, Tanaka S (2018) System and method for providing driving assistance to safely overtake a vehicle. US Patent App. 10/071,748Google Scholar
  20. 20.
    Hare S, Golodetz S, Saffari A, Vineet V, Cheng MM, Hicks SL, Torr PH (2016) Struck: Structured output tracking with kernels. IEEE Trans Pattern Anal Mach Intell 38(10):2096–2109CrossRefGoogle Scholar
  21. 21.
    Henriques JF, Caseiro R, Martins P, Batista J (2015) High-speed tracking with kernelized correlation filters. IEEE Trans Pattern Anal Mach Intell 37(3):583–596CrossRefGoogle Scholar
  22. 22.
    Huang J, Feris RS, Chen Q, Yan S (2015) Cross-domain image retrieval with a dual attribute-aware ranking network. In: Proceedings of the IEEE international conference on computer vision, pp 1062–1070Google Scholar
  23. 23.
    Jenkins MD, Barrie P, Buggy T, Morison G (2016) Extended fast compressive tracking with weighted multi-frame template matching for fast motion tracking. Pattern Recogn Lett 69:82–87CrossRefGoogle Scholar
  24. 24.
    Kim J, Yoon SE (2018) Regional attention based deep feature for image retrieval. In: Proceedings of British machine vision conference (BMVC), Newcastle, EnglandGoogle Scholar
  25. 25.
    Kwon J, Lee KM (2010) Visual tracking decomposition. In: 2010 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 1269–1276Google Scholar
  26. 26.
    Li H, He X, Tao D, Tang Y, Wang R (2018) Joint medical image fusion, denoising and enhancement via discriminative low-rank sparse dictionaries learning. Pattern Recognit 79:130–146CrossRefGoogle Scholar
  27. 27.
    Liu B, Huang J, Kulikowski C, Yang L (2013) Robust visual tracking using local sparse appearance model and k-selection. IEEE Trans Pattern Anal Mach Intell 35(12):2968–2981CrossRefGoogle Scholar
  28. 28.
    Liu W, Zhang Z, Li S, Tao D (2017) Road detection by using a generalized hough transform. Remote Sens 9(6):590CrossRefGoogle Scholar
  29. 29.
    Liu Y, Wang R, Shan S, Chen X (2018) Structure inference net: object detection using scene-level context and instance-level relationships. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 6985–6994Google Scholar
  30. 30.
    Martin R, Arandjelović O (2010) Multiple-object tracking in cluttered and crowded public spaces. In: Advances in visual computing. Springer, pp 89–98Google Scholar
  31. 31.
    Mei X, Ling H (2011) Robust visual tracking and vehicle classification via sparse representation. IEEE Trans Pattern Anal Mach Intell 33(11):2259–2272CrossRefGoogle Scholar
  32. 32.
    Mur-Artal R, Tardós JD (2017) Orb-slam2: an open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Trans Robot 33(5):1255–1262CrossRefGoogle Scholar
  33. 33.
    Nie Y, Chang J, Chaudhry E, Guo S, Smart A, Zhang JJ (2018) Semantic modeling of indoor scenes with support inference from a single photograph. Comput Anim Virtual Worlds 29(3–4):e1825CrossRefGoogle Scholar
  34. 34.
    Ning J, Zhang L, Zhang D, Wu C (2012) Robust mean-shift tracking with corrected background-weighted histogram. IET Comput Vis 6(1):62–69MathSciNetCrossRefGoogle Scholar
  35. 35.
    Ning J, Zhang L, Zhang D, Wu C (2012) Scale and orientation adaptive mean shift tracking. IET Comput Vis 6(1):52–61MathSciNetCrossRefGoogle Scholar
  36. 36.
    Pati YC, Rezaiifar R, Krishnaprasad P (1993) Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: 1993 conference record of the twenty-seventh Asilomar conference on signals, systems and computers, 1993. IEEE, pp 40–44Google Scholar
  37. 37.
    Ross DA, Lim J, Lin RS, Yang MH (2008) Incremental learning for robust visual tracking. Int J Comput Vis 77(1–3):125–141CrossRefGoogle Scholar
  38. 38.
    Sakaridis C, Dai D, Van Gool L (2018) Semantic foggy scene understanding with synthetic data. Int J Comput Vis 126(9):973–992CrossRefGoogle Scholar
  39. 39.
    Schreier DR, Banks C, Mathis J (2018) Driving simulators in the clinical assessment of fitness to drive in sleepy individuals: a systematic review. Sleep Med Rev 38:86–100CrossRefGoogle Scholar
  40. 40.
    Singh AP, Mishra A (2011) Wavelet based watermarking on digital image. Indian J Comput Sci Eng 1(2):86–91Google Scholar
  41. 41.
    Sliti O (2018) Method of optimal directions for visual tracking. In: Proceedings of the 15th ACM SIGGRAPH European conference on visual media production. ACM, p 8Google Scholar
  42. 42.
    Sliti O, Hamam H, Benzarti F, Amiri H (2014) A more robust mean shift tracker using joint monogenic signal analysis and color histogram. In: 2014 22nd international conference on pattern recognition (ICPR). IEEE, pp 2453–2458Google Scholar
  43. 43.
    Sliti O, Hamam H, Amiri H (2018) Clbp for scale and orientation adaptive mean shift tracking. J King Saud University-Comput Inf Sci 30(3):416–429Google Scholar
  44. 44.
    Snekha CS, Birok R et al (2013) Real time object tracking using different mean shift techniques—a review. Int J Soft Comput Eng (IJSCE) 3(3):98–102Google Scholar
  45. 45.
    Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32CrossRefGoogle Scholar
  46. 46.
    Tao D, Tao D, Li X, Gao X (2017) Large sparse cone non-negative matrix factorization for image annotation. ACM Trans Intell Syst Technol (TIST) 8(3):37Google Scholar
  47. 47.
    Tao D, Cheng J, Yu Z, Yue K, Wang L (2018) Domain-weighted majority voting for crowdsourcing. IEEE Transactions on Neural Networks and Learning Systems 30(1):163–174MathSciNetCrossRefGoogle Scholar
  48. 48.
    Tao D, Guo Y, Li Y, Gao X (2018) Tensor rank preserving discriminant analysis for facial recognition. IEEE Trans Image Process 27(1):325–334MathSciNetzbMATHCrossRefGoogle Scholar
  49. 49.
    Wang C, Zhao X, Wu Z, Liu Y (2013) Motion pattern analysis in crowded scenes based on hybrid generative-discriminative feature maps. In: 2013 20th IEEE international conference on image processing (ICIP). IEEE, pp 2837–2841Google Scholar
  50. 50.
    Wu Y, Lim J, Yang MH (2013) Online object tracking: a benchmark. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2411–2418Google Scholar
  51. 51.
    Yang C, Duraiswami R, Davis L (2005) Efficient mean-shift tracking via a new similarity measure. In: IEEE computer society conference on computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 176–183Google Scholar
  52. 52.
    Yilmaz A, Shafique K, Shah M (2003) Target tracking in airborne forward looking infrared imagery. Image Vis Comput 21(7):623–635CrossRefGoogle Scholar
  53. 53.
    Zhang K, Song H (2013) Real-time visual tracking via online weighted multiple instance learning. Pattern Recognit 46(1):397–411MathSciNetzbMATHCrossRefGoogle Scholar
  54. 54.
    Zhang K, Zhang L, Yang MH, Zhang D (2013) Fast tracking via spatio-temporal context learning. arXiv preprint arXiv:1311.1939
  55. 55.
    Zhang K, Zhang L, Liu Q, Zhang D, Yang MH (2014) Fast visual tracking via dense spatio-temporal context learning. In: European conference on computer vision. Springer, pp 127–141Google Scholar
  56. 56.
    Zhou B, Lapedriza A, Khosla A, Oliva A, Torralba A (2018) Places: a 10 million image database for scene recognition. IEEE Trans Pattern Anal Mach Intell 40(6):1452–1464CrossRefGoogle Scholar
  57. 57.
    Zivkovic Z, Krose B (2004) An em-like algorithm for color-histogram-based object tracking. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004, vol 1. IEEE, pp I–798Google Scholar

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Authors and Affiliations

  1. 1.Department of Electrical EngineeringNational Engineering School of TunisTunisTunisia
  2. 2.Department of Electrical EngineeringUniversity of MonctonMonctonCanada

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