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Approximate Proximal Gradient-Based Correlation Filter for Target Tracking in Videos: A Unified Approach

  • Research Article - Computer Engineering and Computer Science
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Abstract

Video cameras are among the most commonly used devices throughout the world which results in imaging technology being one of the most important areas for research and development. Imaging technology requires constant research as it is used in crucial applications such as video conferencing and surveillance. In the field of image processing, motion detection and estimation are fundamental steps in extracting information on objects segmented from their backgrounds. In this paper, a cohesive approach is presented that uses two algorithms for motion estimation and detection. The proposed method is able to detect moving objects using maximum average correlation height (MACH) filter. Upon obtaining the accurate coordinates of an object of interest from the MACH filter, the next part of the algorithm starts tracking the object. For tracking, a particle filter is used to estimate the motion of the object using a Markov chain. To enhance the accuracy of particle filter, an approximate proximal gradient algorithm is employed for unconstrained minimization of the particles which restricts the tracking process to target templates (most essential information) only. Finally, a comparison between the proposed algorithm and recent similar algorithms is made that demonstrates the minimization of tracking errors using the proposed technique.

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References

  1. Wang, D.; Lu, H.; Yang, M.-H.: Least soft-threshold squares tracking. In: CVPR (2013)

  2. Fan, J.; Shen, X.; Wu, Y.: Why are we tracking: a unified approach of tracking and tracking. In: Proceedings of IEEE, Transactions on Image Processing, vol. 22, issue 2, pp. 549–560 (2013)

  3. Avidan, S.: Ensemble tracking. In: CVPR (2005)

  4. Babenko, B.; Yang, M.; Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)

  5. Yin, Z.; Collins, R.: Object detection and tracking after occlusion via numerical hybrid local and global mode seeking. In: CVPR (2008)

  6. Williams, O.; Blake, A.; Cipolla, R.: Sparse Bayesian learning for efficient visual tracking. PAMI 27(8), 1292–1304 (2005)

    Article  Google Scholar 

  7. Black, M.; Jepsen, A.: Eigen tracking: robust matching and tracking of articulated objects using a view-based representation. IJCV 26(1), 63–84 (1998)

    Article  Google Scholar 

  8. Comaniciu, D.; Ramesh, V.; Meer, P.: Kernel based object tracking. PAMI 25(5), 564–577 (2003)

    Article  Google Scholar 

  9. Ross, D.; Lim, J.; Lin, R.; Yang, M.: Incremental learning for robust visual tracking. IJCV 77(1), 125–141 (2008)

    Article  Google Scholar 

  10. Porikli, F.; Tuzel, O; Meer, P.: Covariance tracking using model update based on lie algebra. In: CVPR (2006)

  11. Urban Lisa Data set. Retrieved from http://homepages.inf.ed.ac.uk/rbf/CVonline/Imagedbase.htm (2012)

  12. Yan, C.; Zhang, Y.; Dai, F.; Zhang, J.; Li, L.; Dai, Q.: Efficient parallel HEVC intra prediction on many-core processor. Electron. Lett. 50(11), 805–806 (2014)

    Article  Google Scholar 

  13. Yan, C.; Xie, H.; Yang, D.; Yin, J.; Zhang, Y.; Dai, Q.: Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans. Intell. Transp. Syst. 19, 1–12 (2017)

    Google Scholar 

  14. Yan, C.; Xie, H.; Liu, S.; Yin, J.; Zhang, Y.; Dai, Q.: Effective Uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans. Intell. Transp. Syst. 19, 1–10 (2017)

    Google Scholar 

  15. Farooq, W.; Khan, M.A.; Rehman, S.: A cluster based multicast routing protocol for autonomous unmanned military vehicles (AUMVs) communication in VANET. In: 2016 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube). IEEE (2016)

  16. Ayyaz, Sundus; Rehman, Saad: The most secure smartphone operating system: a survey. World. Acad. Sci. Eng. Technol. Int. J. Comput. Electr. Autom. Control Inf. Eng. 10(2), 324–327 (2016)

    Google Scholar 

  17. Rehman, S.; Riaz, F.; Hassan, A.; Liaquat, M.; Young, R.: Human detection in sensitive security areas through recognition of omega shapes using MACH filters. In SPIE Defense + Security, pp. 947708–947708. International Society for Optics and Photonics (2015)

  18. Leibe, B.; Schindler, K.; Cornelis, N.; Gool, L.V.: Coupled object detection and tracking from static cameras and moving vehicles. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1683–1698 (2008)

    Article  Google Scholar 

  19. Li, Y.; Ai, H.; Yamashita, T.; Lao, S.; Kawade, M.: Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life observers of different life spans. IEEE Trans. Mach. Intell. 30(10), 1728–1740 (2008)

    Article  Google Scholar 

  20. Huang, C.; Wu, B.; Navatia, R.: Robust object tracking by hierarchical association of detection responses. In: Proceedings of European Conference on Computer Vision, vol. 8, pp. 788–801

  21. Yilmaz, A.; Javed, O.; Shah, M.: Object tracking: a survey. ACM Comput. Surv. 38(4), 1–13 (2006)

    Article  Google Scholar 

  22. Yan, C.; Zhang, Y.; Xu, J.; Dai, F.; Li, L.; Dai, Q.; Wu, F.: A highly parallel framework for HEVC coding unit partitioning tree decision on many-core processors. IEEE Signal Process. Lett. 21(5), 573–576 (2014)

    Article  Google Scholar 

  23. Mei, X.; Ling, H.: Robust visual tracking using L1 minimizations. In: ICCV (2009)

  24. Mai, Xue; Ling, Haibein; Yi, Wu; Erik, P.; Blasch, Li Bai: Efficient minimum error bound particle resampling L1 tracker with occlusion detection. IEEE Trans. Image Process. 22(7), 2661–2675 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  25. Mei, X.; Ling, H.; Wu, Y.; Blasch, E.; Bai, L.; Minimum error bound efficient L1 tracker with occlusion detection. In: CVPR (2011)

  26. Liu, B.; Yang, L.; Huang, J.; Meer, P.; Gong, L.; Kulikowski, C.: Robust and fast collaborative tracking with two stage sparse optimization. In: ECCV (2010)

  27. Birch, P.; Mitra, B.; Bangalore, N.M.; Rehman, S.; Young, R.: Approximate bandpass and frequency response models of the difference of Gaussian filter. Opt. Commun. 283(24), 4942–4948 (2010)

    Article  Google Scholar 

  28. Yan, C.; Zhang, Y.; Xu, J.; Dai, F.; Zhang, J.; Dai, Q.; Wu, F.: Efficient parallel framework for HEVC motion estimation on many-core processors. IEEE Trans. Circuits Syst. Video Technol. 24(12), 2077–2089 (2014)

    Article  Google Scholar 

  29. Yan, C.; Zhang, Y.; Dai, F.; Wang, X.; Li, L.; Dai, Q.: Parallel de blocking filter for HEVC on many-core processor. Electron. Lett. 50(5), 367–368 (2014)

    Article  Google Scholar 

  30. Zhong, W.; Lu; H.; Yang; M.-H.: Robust object tracking via sparsity-based collaborative model. In: CVPR (2012)

  31. Yilmaz, A.; Javed, O.; Shah, M.: Object tracking: a survey. ACM Comput. Surv. (CSUR) 38(4), 13 (2006)

    Article  Google Scholar 

  32. Matthews, I.; Ishikawa, T.; Baker, S.: The template update problem. IEEE Trans. Pattern Anal. Mach. Intell. 26(6), 810–815 (2003)

    Article  Google Scholar 

  33. Candes, E.; Romberg, J.; Tao, T.: Stable Signal recovery from incomplete and inaccurate measurements. Commun. Pure Appl. Math. 59(8), 1207–1223 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  34. Marr, D.; Hildreth, E.: Theory of edge detection. Proc. R. Soc. Lond. B 20, 187–217 (1980)

    Google Scholar 

  35. Mahalanobis, A.; Kumar, B.V.K.; Sims, S.R.F.; Epperson, J.F.: Unconstrained correlation filters. Appl. Opt. 33(17), 3751–3759 (1994)

    Article  Google Scholar 

  36. Tehsin, S.; Rehman, S.; Bilal, A.; Young, R.; Chaudry, Q.; Asif, A.: Improved maximum average correlation height filter with adaptive log base selection for object recognition. In: Proceedings of SPIE, Optical Pattern Recognition XXVII (2016)

  37. Refregier, P.: Optimal trade-off filters for noise robustness, sharpness of the correlation peak, and Horner efficiency. Opt. Lett. 16, 829–831 (1991)

    Article  Google Scholar 

  38. Rehman, S.; Bilal, A.; Javed, Y.; Amin, S.; Young, R.: Logarithmically pre-processed EMACH filter for enhanced performance in target recognition. Arab. J. Sci. Eng. 38(2), 3005–3017 (2012)

    Google Scholar 

  39. Rehman, S.; Bone, P.; Banglaore, N.; Young, R.; Chatwin, C.: Object detection and recognition in cluttered scenes using fully scale and in-plane invariant synthetic discriminant function filters. J. Theor. Appl. Inf. 1(1), 15–18 (2007)

    Google Scholar 

  40. Tehsin, S.; Rehman, S., et al.: Improved maximum average correlation height filter with adaptive log base selection for object recognition. In: SPIE Defense + Security. International Society for Optics and Photonics (2016)

  41. Zhou, H.; Chao, T.; MACH filter synthesizing for detecting targets in cluttered environment for gray-scale optical correlator. In: Optical Pattern Recognition (X), vol. 3715 (1991)

  42. Kumar, B.V.K.; Fernandez, J.A.; Rodriguez, A.; Boddeti, V.N.: Recent advances in correlation filter theory and applications. In: Proceedings of SPIE 9094, Optical Pattern Recognition XXV, vol. 909404 (2014). https://doi.org/10.1117/12.2051719

  43. Bone, P.; Young, R.; Chatwin, C.: Position-, rotation-, scale-, and orientation-invariant multiple object recognition from cluttered scenes. Opt. Eng. 45(7), 077203 (2006)

    Article  Google Scholar 

  44. Bone, P.; Kypraios, L.; Young, R.; Chatwin, C.: Fully invariant and object recognition in cluttered sciences. In: Andriesh, A.; Perju, V. (eds.) Invited Paper, Proceedings of the SPIE, Information Technologies. Chisinau, Moldova (2004)

  45. Rehman, S.; Young, R.; Birch, P.; Chatwin, C.; Kypraios, I.: Fully scale and in-plane invariant synthetic discriminant function bandpass difference of gaussian composite filter for object recognition and detection in still images. J. Theor. Appl. Inf. Technol. 5(2), 232–241 (2005)

    Google Scholar 

  46. Bao, C.; Wu, Y.; Ling, H.; Ji, H.: Real time robust L1 tracker using APG approach. In: IEEE Transaction on Computer Vision and Pattern Recognition, pp. 1830–1837 (2012)

  47. Bilal, A.; Rehman, S.; Latif, S.; Synthesis of an adaptive CPR filter for identification of vehicle make & type. In: Software Engineering Conference, pp. 25–29 (2014)

  48. Rehman, S.; Riaz, F.; Hassan, A.; Liaquat, M.; Young, R.; Human detection in sensitive security areas through recognition of omega shapes using MACH filters. In: Proceedings of SPIE 9477, Optical Pattern Recognition XXVI, vol. 947708 (2015). https://doi.org/10.1117/12.2176841

  49. Zhang, T.; Ghanem, B.; Liu, S.; Ahuja, N.: Low-rank sparse learning for robust visual tracking. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part VI, vol. 7577, pp. 470–484. LNCSSpringer, Heidelberg (2012)

    Google Scholar 

  50. Mei, X.; Ling, H.; Wu, Y.; Blasch, H.; Bai, L.; Minimum error bound L1 tracker with occlusion detection. In: IEEE Transaction on Computer Vision and Pattern Recognition, pp. 1257–1264 (2011)

  51. Wu, Y.; Lim, J.; Yang; M.-H.: Online object tracking: a benchmark. In: CVPR (2013)

  52. Wu, Y.; Lim, J.; Yang, M.H.: Online object tracking: a benchmark. In: CVPR (2013)

  53. Danelljan, M.; Khan, F.S.; Felsberg, M.; Weijer, J.V.D.: Adaptive color attributes for real-time visual tracking. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition

  54. Babenko, B.; Yang, M.; Belongie, S.: Visual tracking with online multiple instance learning. In: CVPR (2009)

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

  56. Wu, Y.; Cheng, J.; Wang, J.; Lu, H.; Wang, J.; Ling, H.; Blasch, E.; Bai, L.: Real-time probabilistic covariance tracking with efficient model update. In: IEEE T-IP (2012)

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Authors and Affiliations

  1. Department of Computer Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan

    Haris Masood, Saad Rehman, Aimal Khan, Farhan Riaz, Ali Hassan & Muhammad Abbas

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  1. Haris Masood
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  2. Saad Rehman
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  3. Aimal Khan
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  4. Farhan Riaz
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  5. Ali Hassan
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  6. Muhammad Abbas
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Correspondence to Haris Masood.

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Masood, H., Rehman, S., Khan, A. et al. Approximate Proximal Gradient-Based Correlation Filter for Target Tracking in Videos: A Unified Approach. Arab J Sci Eng 44, 9363–9380 (2019). https://doi.org/10.1007/s13369-019-03861-3

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  • DOI: https://doi.org/10.1007/s13369-019-03861-3

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