Multimedia Tools and Applications

, Volume 76, Issue 3, pp 4617–4634 | Cite as

Real-time visual tracking based on improved perceptual hashing

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Abstract

Video object tracking represents a very important computer vision domain. In this paper, a perceptual hashing based template-matching method for object tracking is proposed to efficiently track objects in challenging video sequences. In the tracking process, we first apply three existing basic perceptual hashing techniques to visual tracking, namely average hash (aHash), perceptive hash (pHash) and difference hash (dHash). Compared with previous tracking methods such as mean-shift or compressive tracking (CT), perceptual hashing-based tracking outperforms in terms of efficiency and accuracy. In order to further improve the accuracy of object localization and the robustness of tracking, we propose Laplace-based Hash (LHash) and Laplace-based Difference Hash (LDHash). By qualitative and quantitative comparison with some representative tracking algorithms, experimental results show that our improved perceptual hashing-based tracking algorithms perform favorably against the state-of-the-art algorithms under various challenging environments in terms of time cost, accuracy and robustness. Since our improved perceptual hashing can be a compact and efficient representation of objects, it can be further applied to fusing with depth information for more robust RGB-D video tracking.

Keywords

Visual tracking Perceptual hashing AHash PHash DHash 

References

  1. 1.
    Altinok A, El-Saban M, Peck AJ (2006) Activity analysis in microtubule videos by mixture of hidden Markov models. 2006 I.E. Conf Comput Vision Pattern Recognit 2:1662–1669Google Scholar
  2. 2.
    Avidan S (2004) Support vector tracking. IEEE Trans Pattern Anal Mach Intell 26(8):1064–1072CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    Bhattacharjee S, Kutter M (1998) Compression tolerant image authentication. Proc Int Conf Image Process 1:435–439Google Scholar
  5. 5.
    Black MJ, Jepson AD (1998) Eigentracking: robust matching and tracking of articulated objects using a view-based representation. Int J Comput Vis 26(1):63–84CrossRefGoogle Scholar
  6. 6.
    Bradski G, Kaehler A (2008) Learning OpenCV: computer vision with the OpenCV library. “ O’Reilly Media, Inc.”Google Scholar
  7. 7.
    Bulling A, Gellersen H (2010) Toward mobile eye-based human-computer interaction. Pervasive Comput 9(4):8–12CrossRefGoogle Scholar
  8. 8.
    Bulling A, Ward J, Gellersen H et al (2011) Eye movement analysis for activity recognition using electrooculography. IEEE Trans Pattern Anal Mach Intell 33(4):741–753CrossRefGoogle Scholar
  9. 9.
    Cesetti A, Frontoni E, Mancini A (2010) A vision-based guidance system for UAV navigation and safe landing using natural landmarks. Selected papers from the 2nd International Symposium on UAVs, Reno, Nevada, USA June 8–10, 2009. Springer, Netherlands, pp 233–257MATHGoogle Scholar
  10. 10.
    Chen J (2010) UAV-guided navigation for ground robot tele-operation in a military reconnaissance environment. Ergonomics 53:940–950CrossRefGoogle Scholar
  11. 11.
    Chen N, Xiao HD, Wan W (2011) Audio hash function based on non-negative matrix factorisation of mel-frequency cepstral coefficients. Information Security, IET 5(1):19–25CrossRefGoogle Scholar
  12. 12.
    Comaniciu D, Ramesh V, Meer P (2000) Real-time tracking of non-rigid objects using mean shift. IEEE Conf Comput Vision Pattern Recognit 2:142–149Google Scholar
  13. 13.
    Coşkun B, Sankur B (2004) Robust video hash extraction. Proc IEEE Conf Sign Process Commun Appl:292–295Google Scholar
  14. 14.
    Jia Z, Balasuriya A, Challa S (2008) Autonomous vehicles navigation with visual target tracking: technical approaches. Algorithms 1(2):153–182CrossRefGoogle Scholar
  15. 15.
    Jie Z (2013) A novel block-DCT and PCA based image perceptual hashing algorithm. arXiv preprint arXiv:1306.4079Google Scholar
  16. 16.
    Kalal Z, Matas J, Mikolajczyk K (2010) Pn learning: bootstrapping binary classifiers by structural constraints. IEEE Conf Comput Vision Pattern Recognit:49–56Google Scholar
  17. 17.
    Karavasilis V, Nikou C, Likas A (2011) Visual tracking using the earth mover’s distance between Gaussian mixtures and Kalman filtering. Image Vis Comput 29(5):295–305CrossRefGoogle Scholar
  18. 18.
    Kwon J, Lee KM (2010) Visual tracking decomposition. IEEE Conf Comput Vision Pattern Recognit:1269–1276Google Scholar
  19. 19.
    Kwon J, Lee KM (2010) Visual tracking decomposition. (CVPR). IEEE Conf Comput Vision Pattern Recognit 1269–1276Google Scholar
  20. 20.
    Laradji IH, Ghouti L, Khiari EH (2013) Perceptual hashing of color images using hypercomplex representations. IEEE Int Conf Imag Process: 4402–4406Google Scholar
  21. 21.
    Li J, Allinson NM (2008) A comprehensive review of current local features for computer vision. Neurocomputing 71(10):1771–1787CrossRefGoogle Scholar
  22. 22.
    Li X, Shen C, Dick A, et al. (2013) Learning compact binary codes for visual tracking. IEEE Conf Comput Vision Pattern Recognit:2419–2426Google Scholar
  23. 23.
    Li H, Shen C, Shi Q (2011) Real-time visual tracking using compressive sensing. IEEE Conf Computer Vision Pattern Recognition: 1305–1312Google Scholar
  24. 24.
    Liu L, Shao L (2013) Learning discriminative representations from RGB-D video data. Proc Twenty-Third Int Joint Conf Artificial Intell. AAAI Press, 1493–1500Google Scholar
  25. 25.
    Liu L, Yu M, Shao L (2015) Multiview alignment hashing for efficient image search. IEEE Trans Image Process 24(3):956–966MathSciNetCrossRefGoogle Scholar
  26. 26.
    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
  27. 27.
    Micalizio R, Scala E, Torasso P (2011) Intelligent supervision for robust plan execution. AI* IA 2011: artificial intelligence around man and beyond. Springer, Berlin Heidelberg, pp 151–163CrossRefGoogle Scholar
  28. 28.
    Monga V, Evans BL (2006) Perceptual image hashing via feature points: performance evaluation and tradeoffs. IEEE Trans Image Process 15(11):3452–3465CrossRefGoogle Scholar
  29. 29.
    Narasimha MJ, Peterson AM (1978) On the computation of the discrete cosine transform. IEEE Trans Commun 26(6):934–936CrossRefMATHGoogle Scholar
  30. 30.
    Newcombe R, Fox D, Seitz S (2015) DynamicFusion: reconstruction and tracking of non-rigid scenes in real-time. IEEE Comput Vision Pattern Recognition: 343–352Google Scholar
  31. 31.
    Perng MH, Chang HH (1993) Intelligent supervision of servo control. control theory and applications. IEE Proc D IET 140(6):405–412CrossRefMATHGoogle Scholar
  32. 32.
    Santner J, Leistner C, Saffari A, et al. (2010) Prost: parallel robust online simple tracking. 2010 I.E. Conf Comput Vision Pattern Recognit:723–730Google Scholar
  33. 33.
    Shao L, Liu L, Li X (2014) Feature learning for image classification via multiobjective genetic programming. IEEE Trans Neural Networks Learn Syst 25(7):1359–1371CrossRefGoogle Scholar
  34. 34.
    Wang L, Liu T, Wang G, Chan KL, Yang Q (2015) Video tracking using learned hierarchical features. IEEE Trans Image Process 24(4):1424–1435MathSciNetCrossRefGoogle Scholar
  35. 35.
    Wang PK, Torrione PA, Collins LM, et al. (2012) Rapid position estimation and tracking for autonomous driving. SPIE defense, security, and sensing. Int Soc Optics Photonics:83871I–83871IGoogle Scholar
  36. 36.
    Watson AB (1994) Image compression using the discrete cosine transform. Mathematica J 4(1):81MathSciNetGoogle Scholar
  37. 37.
    Weng L, Preneel B (2009) Shape-based features for image hashing. 2009. IEEE Int Conf Multimed Expo: 1074–1077Google Scholar
  38. 38.
    Wen-Hsiung C, Smith C, Fralick S (1977) A fast computational algorithm for the discrete cosine tranfsorm. IEEE Trans Commun 25(9):1004–1009CrossRefMATHGoogle Scholar
  39. 39.
    Yang B, Gu F, Niu X (2006) Block mean value based image perceptual hashing. Int Conf Intell Inform Hiding Multimed Sign Process:167–172Google Scholar
  40. 40.
    Yang H, Shao L, Zheng F et al (2011) Recent advances and trends in visual tracking: a review. Neurocomputing 74(18):3823–3831CrossRefGoogle Scholar
  41. 41.
    Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv (CSUR) 38(4):13CrossRefGoogle Scholar
  42. 42.
    Yoon Y, Yun W, Yoon H, et al. (2014) Real-time visual target tracking in RGB-D data for person-following robots. Pattern Recognition (ICPR), 2014 22nd Int Conf. IEEE, 2227–2232Google Scholar
  43. 43.
    Yu M, Liu L, Shao L (2015) Structure-preserving binary representations for RGB-D action recognition. IEEE Trans Pattern Anal Mach Intell. doi:10.1109/TPAMI.2015.2491925 Google Scholar
  44. 44.
    Zhang P, Li N (2005) The intellectual development of human-computer interaction research: a critical assessment of the MIS literature (1990–2002). J Assoc Inf Syst 6(11):227–292Google Scholar
  45. 45.
    Zhang BC, Li ZG, Perina, A (2016) Adaptive local movement modeling for object tracking, IEEE TCSVTGoogle Scholar
  46. 46.
    Zhang B, Perina A, Li Z, Murino V, Liu J, Ji R (2016) Bounding multiple gaussians uncertainty with application to object tracking. Int J Comput Vision:1–16Google Scholar
  47. 47.
    Zhang S, Yao H, Zhou H et al (2013) Robust visual tracking based on online learning sparse representation. Neurocomputing 100:31–40CrossRefGoogle Scholar
  48. 48.
    Zhang K, Zhang L, Yang MH (2012) Real-time compressive tracking. computer vision–ECCV 2012. Springer, Berlin Heidelberg, pp 864–877Google Scholar
  49. 49.
    Zhu F, Shao L (2014) Weakly-supervised cross-domain dictionary learning for visual recognition. Int J Comput Vis 109(1–2):42–59CrossRefMATHGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Mengjuan Fei
    • 1
  • Zhaojie Ju
    • 2
  • Xiantong Zhen
    • 3
  • Jing Li
    • 4
  1. 1.Institute of Cyber-Systems and controlZhejiang UniversityHangZhouChina
  2. 2.Intelligent Systems and Biomedical Robotics Group, School of ComputingUniversity of PortsmouthPortsmouthUK
  3. 3.The University of Western OntarioLondonCanada
  4. 4.Laboratory of Ubiquitous Vision Perception and Intelligent Computing, School of Information EngineeringNanchang UniversityNanchangChina

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