Soft Computing

, Volume 22, Issue 1, pp 231–242 | Cite as

Group object detection and tracking by combining RPCA and fractal analysis

  • Longxin LinEmail author
  • Weiwei LinEmail author
  • Sibin Huang
Methodologies and Application


Automatic video analysis is a hot research topic in the field of computer vision and has broad application prospects. It usually consists of three key steps: object detection, object tracking and behavior recognition. Usually, object detection is just considered as the precondition of object tracking, and the correlation between them is very little. So, existing video analysis solutions treat them as independent procedures and execute them separately. Actually, object detection and tracking are related and the effective combination of them can improve the performance of video analysis. This paper mainly studies object detection and tracking, and tries to utilize the outputs of them to optimize their performance by each other. For this purpose, a unified algorithm framework called group object detection and tracking is presented, which detects moving objects by robust principle component analysis (RPCA) and Graph Cut algorithm and tracks objects via fractal analysis simultaneously. The multi-fractal spectrum (MFS) constrain and Graph Cut improve the complement of object detection, which will bring more exact tracking feature. At the same time, the successful results from tracking can provide optimal constrain for object detection in an opposite manner. Therefore, object detection and tracking are grouped and can be improved by an iterative RPCA algorithm. The experimental results of simulation and real sequence demonstrate that the proposed algorithm is more robust and outperforms state-of-art algorithms in object detection and tracking.


Fractal analysis Robust principle component analysis Object detection Object tracking Motion segmentation Multi-fractal spectrum 



We want to thank the helpful comments and suggestions from the anonymous reviewers. This work is partially supported by the National Natural Science Foundation of China (Grant Nos. 61501207 and 61402183), Guangdong Natural Science Foundation (Grant Nos. S2012030006242 and S2013040012449), Guangdong Provincial Scientific and Technological Projects (Grant Nos. 2013B090500030, 2016A010119171, 2016A010101018, 2016A010101007, and 2016B090918021), Guangzhou Scientific and Technological Projects (Grant Nos. 2013Y2-00065, 2013J4300056, 2014Y2-00133, 201601010314, 201607010048 and 201604010040).

Compliance with ethical standards

Conflicts of interest

The authors declare that they have no conflict of interest.


  1. Alper Y, Omar J, Mubarak S (2006) Object tracking: a survey. ACM Comput Surv 38(4):1–35Google Scholar
  2. Amiaz T, Kiryati N (2006) Piecewise-smooth dense optical flow via level sets. Int J Comput Vis 68(2):111–124CrossRefGoogle Scholar
  3. Avidan S (2004) Support vector tracking. IEEE Trans Pattern Anal Mach Intell 26(8):1064–1072CrossRefGoogle Scholar
  4. Bai X, Wang J, Simons D, Sapiro G (2009) Video snapcut: robust video object cutout using localized classifiers. ACM Transactions on Graphics (TOG) 28(3):70CrossRefGoogle Scholar
  5. Barnich O, Van Droogenbroeck M (2011) Vibe: a universal background subtraction algorithm for video sequences. IEEE Trans Image Process 20(6):1709–1724MathSciNetCrossRefzbMATHGoogle Scholar
  6. Bertalmio M, Sapiro G, Randall G (2000) Morphing active contours. IEEE Trans Pattern Anal Mach Intell 22(7):733–737CrossRefGoogle Scholar
  7. Black MJ, Anandan P (1996) The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput Vis Image Underst 63(1):75–104CrossRefGoogle Scholar
  8. Bouwmans T (2014) Traditional and recent approaches in background modeling for foreground detection: an overview. Comput Sci Rev 11:31–66CrossRefzbMATHGoogle Scholar
  9. Boykov Y, Veksler O, Zabih R (2001) Fast approximate energy minimization via graph cuts. IEEE Trans Pattern Anal Mach Intell 23(11):1222–1239CrossRefGoogle Scholar
  10. Broida TJ, Chellappa R (1986) Estimation of object motion parameters from noisy images. IEEE Trans Pattern Anal Mach Intell 1:90–99CrossRefGoogle Scholar
  11. Brox T, Malik J, (2010) Object segmentation by long term analysis of point trajectories. In: Computer vision-ECCV 2010. Springer, Berlin, pp 282–295Google Scholar
  12. Brox T, Bruhn AES, Weickert J, (2006) Variational motion segmentation with level sets. In: Computer vision-ECCV 2006. Springer, Berlin, pp 471–483Google Scholar
  13. Brutzer S, Hoferlin B, Heidemann G (2011) Evaluation of background subtraction techniques for video surveillanceGoogle Scholar
  14. Cand ES, Emmanuel J, Li X, Ma Y, Wright J (2011) Robust principal component analysis? J ACM (JACM) 58(3):11MathSciNetzbMATHGoogle Scholar
  15. Chan AB, Vasconcelos N (2009) Layered dynamic textures. IEEE Trans Pattern Anal Mach Intell 31(10):1862–1879CrossRefGoogle Scholar
  16. Chen B, Shu H, Coatrieux G, Chen G, Sun X, Coatrieux JL (2015) Color image analysis by quaternion-type moments. J Math Imaging Vis 51(1):124–144MathSciNetCrossRefzbMATHGoogle Scholar
  17. Comaniciu D, Meer P (1999) Mean shift analysis and applicationsGoogle Scholar
  18. Fazekas SA, Amiaz T, Chetverikov D, Kiryati N (2009) Dynamic texture detection based on motion analysis. Int J Comput Vis 82(1):48–63CrossRefGoogle Scholar
  19. Fortun D, Bouthemy P, Kervrann C (2015) Optical flow modeling and computation: a survey. Comput Vis Image Underst 134:1–21CrossRefzbMATHGoogle Scholar
  20. Gauglitz S, Llerer THO, Turk M (2011) Evaluation of interest point detectors and feature descriptors for visual tracking. Int J Comput Vis 94(3):335–360CrossRefzbMATHGoogle Scholar
  21. Han B, Comaniciu D, Zhu Y, Davis LS (2008) Background subtraction techniques: a review. IEEE Trans Pattern Anal Mach Intell 30(7):1186–1197CrossRefGoogle Scholar
  22. Kang J, Cohen I, Medioni G (2004) Object reacquisition using invariant appearance modelGoogle Scholar
  23. Kim K, Chalidabhongse TH, Harwood D, Davis L (2005) Real-time foreground-background segmentation using codebook model. Real-time Imaging 11(3):172–185CrossRefGoogle Scholar
  24. Li L, Huang W, Gu IYH, Tian Q (2004) Statistical modeling of complex backgrounds for foreground object detection. IEEE Trans Image Process 13(11):1459–1472CrossRefGoogle Scholar
  25. Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518CrossRefGoogle Scholar
  26. Lin L, Lin W, Xiao W, Huang S (2015) An optimized video synopsis algorithm and its distributed processing model. Soft Comput 1–13Google Scholar
  27. Mikolajczyk K, Schmid C (2004) Scale and affine invariant interest point detectors. Int J Comput Vis 60(1):63–86CrossRefGoogle Scholar
  28. Mittal A, Paragios N (2004) Motion-based background subtraction using adaptive kernel density estimationGoogle Scholar
  29. Ochs P, Brox T (2011) Object segmentation in video: a hierarchical variational approach for turning point trajectories into dense regionsGoogle Scholar
  30. Ochs P, Malik J, Brox T (2014) Segmentation of moving objects by long term video analysis. IEEE Trans Pattern Anal Mach Intell 36(6):1187–1200CrossRefGoogle Scholar
  31. Oliver NM, Rosario B, Pentland AP (2000) A bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal Mach Intell 22(8):831–843CrossRefGoogle Scholar
  32. Pan Z, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61(2):166–176CrossRefGoogle Scholar
  33. Paskaš MP, Reljin BD, Reljin IS (2016) Revision of multifractal descriptors for texture classification based on mathematical morphology. Pattern Recogn LettGoogle Scholar
  34. Rittscher J, Kato J, Joga SEB, Blake A, (2000) A probabilistic background model for tracking. In: Computer visionECCV 2000. Springer, Berlin, pp 336–350Google Scholar
  35. Rother C, Kolmogorov V, Blake A (2004) Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph (TOG) 23(3):309–314CrossRefGoogle Scholar
  36. Sato K, Aggarwal JK (2004) Temporal spatio-velocity transform and its application to tracking and interaction. Comput Vis Image Underst 96(2):100–128CrossRefGoogle Scholar
  37. Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRefGoogle Scholar
  38. Sobral A, Vacavant A (2014) A comprehensive review of background subtraction algorithms evaluated with synthetic and real videos. Comput Vis Image Underst 122:4–21CrossRefGoogle Scholar
  39. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time trackingGoogle Scholar
  40. Tao H, Sawhney HS, Kumar R (2002) Object tracking with bayesian estimation of dynamic layer representations. IEEE Trans Pattern Anal Mach Intell 24(1):75–89CrossRefGoogle Scholar
  41. Veenman CJ, Reinders MJT, Backer E (2001) Resolving motion correspondence for densely moving points. IEEE Trans Pattern Anal Mach Intell 23(1):54–72CrossRefGoogle Scholar
  42. Wang H, Suter D, (2006) A novel robust statistical method for background initialization and visual surveillance. In: Computer vision-ACCV 2006. Springer, Berlin, pp 328–337Google Scholar
  43. Xia Z, Wang X, Sun X, Liu Q, Xiong N (2016) Steganalysis of lsb matching using differences between nonadjacent pixels. Multimed Tools Appl 75(4):1947–1962CrossRefGoogle Scholar
  44. Xiaowei Z, Can Y, Weichuan Y (2013) Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans Pattern Anal Mach Intell 35(3):597–610CrossRefGoogle Scholar
  45. Xu Y, Huang S-B, Ji H, Fermuller C (2009a) Combining powerful local and global statistics for texture descriptionGoogle Scholar
  46. Xu Y, Huang S, Ji H (2009b) Integrating local feature and global statistics for texture analysisGoogle Scholar
  47. Xu Y, Ji H, Fermür C (2009c) Viewpoint invariant texture description using fractal analysis. Int J Comput Vis 83(1):85–100CrossRefGoogle Scholar
  48. Yong X, Quan Y, Zhang Z, Ling H, Ji H (2015) Classifying dynamic textures via spatiotemporal fractal analysis. Pattern Recogn 48(10):3239–3248CrossRefGoogle Scholar
  49. Yan J, Pollefeys M, (2006) A general framework for motion segmentation: independent, articulated, rigid, non-rigid, degenerate and non-degenerate. In: Computer vision-ECCV 2006. Springer, Berlin, pp 94–106Google Scholar
  50. Yitzhaky Y, Peli E (2003) A method for objective edge detection evaluation and detector parameter selection. IEEE Trans Pattern Anal Mach Intell 25(8):1027–1033CrossRefGoogle Scholar
  51. Zeković A, Reljin I (2013) Multifractal and inverse multifractal analysis of multiview 3d video. In: Telecommunications forum (TELFOR), 2013 21st. IEEE, pp 753–756Google Scholar
  52. Zheng Y, Jeon B, Danhua X, Wu QM, Zhang H (2015) Image segmentation by generalized hierarchical fuzzy c-means algorithm. J Intell Fuzzy Syst 28(2):961–973Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.College of Information Science and TechnologyJinan UniversityGuangzhouChina
  2. 2.School of Computer Engineering and ScienceSouth China University of TechnologyGuangzhouChina
  3. 3.Guangzhou Pixcoo Information and Technology LTDGuangzhouChina

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