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An advanced association of particle filtering and kernel based object tracking

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Abstract

The association approaches of particle filter (PF) and kernel based object tracking (KBOT) are widely used in visual tracking. Specially, a compact association approach is proposed, which is based on an incremental Bhattacharyya dissimilarity (IBD) and condition number. It is advanced approach, but this paper found that it cannot guarantee the stable tracking and the high accuracy of tracking in any cases. To solve these problems, we first introduces an asymmetric incremental Bhattacharyya similarity (AIBS) instead of IBD. AIBS is defined by incorporating an asymmetric incremental similarity matrix (AISM) and enables to ensure the stability of tracking. Then, we propose a boosting–refining approach, which is boosting the particles positioned at the ill-posed condition instead of eliminating the ill-posed particles to refine the particles. It enables the estimation of the object stare to obtatin high accuracy. Also, We propose also the resampling-refining algorithm to advance the performance of the framework based on the association of PF and KBOT. Finally, we test the stability and the accuracy of the association approaches that we proposed in this paper, on a synthesized image sequence and several real image sequences. Experimental results demonstrate that our approaches have the promising discriminative capability in comparison with other ones.

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References

  1. Bai K, Liu W (2007) Improved object tracking with particle filter and mean shift. In: Proceedings of the IEEE international conference on automation and logistics. Jinan, pp 431–435

  2. Birchfield ST, Rangarajan S (2005) Spatiograms versus histograms for region-based tracking. In: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), pp 1158–1163

  3. Comaniciu D, Ramesh V, Meer P (2003) Kernel based object tracking. IEEE Trans Pattern Anal Mach Intell 25(5):564–577

    Article  MATH  Google Scholar 

  4. Fan Z, Yang M, Wu Y, Hua G, Yu T (2006) Efficient optimal kernel placement for reliable visual tracking. In: Proceedings of the IEEE computer society conference on Computer Vision and Pattern Recognition (CVPR’06), vol 1, pp 658–665

  5. Gao C, Chen W (2011) Ground moving target tracking with VS-IMM using mean shift unscented particle filter. Chin J Aeronaut 24(5):622–630

    Article  MathSciNet  Google Scholar 

  6. Han B, Comaniciu D, Zhu Y, Davis L (2004) Incremental density approximation and kernel-based Bayesian filtering for object tracking. In: Proceedings of the IEEE Computer Vision and Pattern Recognition (CVPR), pp 638–644

  7. Isard M, Blake A (1998) Condensation-conditional density propagation for visual tracking. Int J Comput Vis 29(14):5–28

    Article  MATH  Google Scholar 

  8. Jia J, Wang Q, Chai Y, Zhao R (2006) Object tracking by multi-degrees of freedom mean shift procedure combined with the kalman particle filter algorithm. In: Proceedings of the IEEE international conference on machine learning and cybernetics. Dalian, pp 3793–3797

  9. Khan ZH, Gu IYH, Backhouse A (2009) Joint particle filters and multi-mode anisotropic mean shift for robust tracking of video objects with partitioned areas. In: IEEE International Conference on Image Processing (ICIP 2009), pp 4077–4080

  10. Khan ZH, Gu IYH, Backhouse AG (2011) Robust visual object tracking using multi-mode anisotropic mean shift and particle filters. IEEE Trans Circ Syst Video Tech 21(1):74–87

    Article  MATH  Google Scholar 

  11. Le P, Duong A D, Vu H Q, Pham N T (2009) Adaptive hybrid mean shift and particle filter. In: International conference on computing and communication technologies, 2009. RIVF ’09, pp 1–4

  12. Liu H, Li J, Qian Y, Liu Q (2008) Robust multi-target tracking using mean shift and particle filter with target model update. In: Proceedings of the 3rd international conference on computer Vision Theory and Applications (VISAPP 2008), vol 2, pp 605–610

  13. Maggio E, Cavallaro A (2005) Hybrid particle filter and mean shift tracker with adaptive transition model. In: Proceeding of IEEE signal processing society International Conference on Acoustics, Speech, and Signal Processing (ICASSP). Philadelphia

  14. Shan C, Tan T, Wei Y (2007) Real-time hand tracking using a mean shift embedded particle filter. Pattern Recog 40:1958–1970

    Article  MATH  Google Scholar 

  15. Wang X, Zha Y, Bi D (2006) An adaptive mean shift particle filter for moving objects tracking. In: Proceedings SPIE 6279, 27th international congress on high-speed photography and photonics, vol 6279

  16. Wang H, Suter D, Schindler K, Shen C (2007) Adaptive object tracking based on an effective appearance filter. IEEE Trans Pattern Anal Mach Intell 29(9):1661–1667

    Article  Google Scholar 

  17. Wang H, Yang B, Tian G, Men A (2009) Object tracking by applying mean-shift algorithm into particle filtering. In: 2nd IEEE International Conference on Broadband Network & Multimedia Technology, 2009. IC-BNMT ’09, pp 550–554

  18. Wang J, Liang W (2011) Robust tracking algorithm using mean-shift and particle filter. In: The 4th International Conference on Machine Vision (ICMV2011): computer vision and image analysis; pattern recognition and basic technologies

  19. Xu F, Cheng J, Wang C (2008) Real time face tracking using particle filtering and mean shift. In: Proceedings of the IEEE international conference on automation and logistics. Qingdao, pp 2252–2255

  20. Yang C, Duraiswami R, Davis L (2005) Efficient mean-shift tracking via a new similarity measure. In: Proceedings of the IEEE computer society conference on Computer Vision and Pattern Recognition (CVPR’05), vol 2, pp 1158–1163

  21. Yang W, Hu S, Li J, Shi D (2008) Robust tracking in FLIR imagery by mean shift combined with particle filter algorithm. In: IEEE international symposium on proceeding of knowledge acquisition and modeling workshop, pp 761–764

  22. Yao A, Wang G, Lin X, Chai X (2010) An incremental Bhattacharyya dissimilarity measure for particle filteirng. Pattern Recog 43(4):1244–1256

    Article  Google Scholar 

  23. Yao A, Lin X, Wang G, Yu S (2012) A compact association of particle filtering and kernel based object tracking. Pattern Recog 45(7):2584–2597

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

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Authors and Affiliations

  1. Intelligent and Distributed Computing Laboratory, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China

    Gwangmin Choe, Tianjiang Wang & Fang Liu

  2. Visual Information Processing Laboratory, School of Computer Science and Technology, Kim Il Sung University, Pyongyang, Democratic People’s Republic of Korea

    Chunhwa Choe & Manhung Jong

Authors
  1. Gwangmin Choe
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  2. Tianjiang Wang
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  3. Fang Liu
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  4. Chunhwa Choe
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  5. Manhung Jong
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Correspondence to Gwangmin Choe.

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Choe, G., Wang, T., Liu, F. et al. An advanced association of particle filtering and kernel based object tracking. Multimed Tools Appl 74, 7595–7619 (2015). https://doi.org/10.1007/s11042-014-1993-3

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